Rozprawy doktorskie: „Computational social networks”

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Hamdi, Sana. "Computational models of trust and reputation in online social networks." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLL001/document.

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Les réseaux sociaux ont connu une évolution dramatique et ont été utilisés comme des moyens pour exercer plusieurs activités. En fait, via les réseaux sociaux, les utilisateurs peuvent découvrir, gérer et partager leurs expériences et avis en ligne. Cependant, la nature ouverte et décentralisée des réseaux sociaux les rend vulnérables à l'apparition des utilisateurs malveillants. Par conséquent, les utilisateurs éventuels peuvent faire face à plusieurs de problèmes liés à la confiance. Ainsi, une évaluation de confiance effective et efficace est très importante pour la prise de décisions par ces utilisateurs. En effet, elle leur fournit des informations précieuses leur permettant de faire la différence entre ceux dignes et indignes de confiance. Cette thèse a pour but de fournir des méthodes de gestion de confiance et de réputation des utilisateurs des réseaux sociaux efficaces et qui peuvent être présentées par les quatre contributions suivantes. La première contribution présente une complexe extraction des contextes et des intérêts des utilisateurs, où les informations contextuelles sociales complexes sont prises en compte, reflétant mieux les réseaux sociaux. De plus, nous proposons un enrichissement de l'ontologie Dbpedia par des concepts de folksonomies.Ensuite, nous proposons une approche de gestion de la confiance, intitulée IRIS, permettant la génération du réseau de confiance et le calcul de la confiance directe. Cette approche considère les activités sociales des utilisateurs incluant leurs relations sociales, préférences et interactions.La troisième contribution de cette thèse est la gestion de transitivité de confiance dans les réseaux sociaux. En fait, c'est nécessaire et significatif d'évaluer la confiance entre deux participants n’ayant pas des interactions directes. Nous proposons ainsi, un modèle d'inférence de confiance, appelé TISON, pour évaluer la confiance indirecte dans les réseaux sociaux.La quatrième contribution de cette thèse consiste à gérer la réputation des utilisateurs des réseaux sociaux. Pour ce faire, nous proposons deux nouveaux algorithmes. Nous présentons un nouvel algorithme exclusif pour la classification des utilisateurs basés sur leurs réputations, appelé le RePC. De plus, nous proposons un deuxième algorithme, FCR, qui présente une extension floue de RePC. Pour les approches proposées, nous avons conduits différentes expérimentations sur des ensembles de données réels ou aléatoires. Les résultats expérimentaux ont démontré que nos algorithmes proposés produisent de meilleurs résultats, en termes de qualité des résultats livrés et d’efficacité, par rapport à différentes approches introduites dans littérature Online Social Networks (OSNs) have known a dramatic increase and they have been used as means for a rich variety of activities. In fact, within OSNs, usersare able to discover, extend, manage, and leverage their experiences and opinionsonline. However, the open and decentralized nature of the OSNs makes themvulnerable to the appearance of malicious users. Therefore, prospective users facemany problems related to trust. Thus, effective and efficient trust evaluation isvery crucial for users’ decision-making. It provides valuable information to OSNsusers, enabling them to make difference between trustworthy and untrustworthyones. This thesis aims to provide effective and efficient trust and reputationmanagement methods to evaluate trust and reputation of OSNs users, which canbe divided into the following four contributions.The first contribution presents a complex trust-oriented users’ contexts andinterests extraction, where the complex social contextual information is taken intoaccount in modelling, better reflecting the social networks in reality. In addition,we propose an enrichment of the Dbpedia ontology from conceptualizations offolksonomies.We second propose the IRIS (Interactions, Relationship types and Interest Similarity)trust management approach allowing the generation of the trust networkand the computation of direct trust. This model considers social activities of usersincluding their social relationships, preferences and interactions. The intentionhere is to form a solid basis for the reputation and indirect trust models.The third contribution of this thesis is trust inference in OSNs. In fact, it isnecessary and significant to evaluate the trust between two participants whomhave not direct interactions. We propose a trust inference model called TISON(Trust Inference in Social Networks) to evaluate Trust Inference within OSNs.The fourth contribution of this thesis consists on the reputation managementin OSNs. To manage reputation, we proposed two new algorithms. We introducea new exclusive algorithm for clustering users based on reputation, called RepC,based on trust network. In addition, we propose a second algorithm, FCR, whichis a fuzzy extension of RepC.For the proposed approaches, extensive experiments have been conducted onreal or random datasets. The experimental results have demonstrated that ourproposed algorithms generate better results, in terms of the utility of delivered results and efficiency, than do the pioneering approaches of the literature

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Grabowicz, Przemyslaw Adam. "Complex networks approach to modeling online social systems. The emergence of computational social science." Doctoral thesis, Universitat de les Illes Balears, 2014. http://hdl.handle.net/10803/131220.

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This thesis is devoted to quantitative description, analysis, and modeling of complex social systems in the form of online social networks. Statistical patterns of the systems under study are unveiled and interpreted using concepts and methods of network science, social network analysis, and data mining. A long-term promise of this research is that predicting the behavior of complex techno-social systems will be possible in a way similar to contemporary weather forecasting, using statistical inference and computational modeling based on the advancements in understanding and knowledge of techno-social systems. Although the subject of this study are humans, as opposed to atoms or molecules in statistical physics, the availability of extremely large datasets on human behavior permits the use of tools and techniques of statistical physics. This dissertation deals with large datasets from online social networks, measures statistical patterns of social behavior, and develops quantitative methods, models, and metrics for complex techno-social systems. La presente tesis está dedicada a la descripción, análisis y modelado cuantitativo de sistemas complejos sociales en forma de redes sociales en internet. Mediante el uso de métodos y conceptos provenientes de ciencia de redes, análisis de redes sociales y minería de datos se descubren diferentes patrones estadísticos de los sistemas estudiados. Uno de los objetivos a largo plazo de esta línea de investigación consiste en hacer posible la predicción del comportamiento de sistemas complejos tecnológico-sociales, de un modo similar a la predicción meteorológica, usando inferencia estadística y modelado computacional basado en avances en el conocimiento de los sistemas tecnológico-sociales. A pesar de que el objeto del presente estudio son seres humanos, en lugar de los átomos o moléculas estudiados tradicionalmente en la física estadística, la disponibilidad de grandes bases de datos sobre comportamiento humano hace posible el uso de técnicas y métodos de física estadística. En el presente trabajo se utilizan grandes bases de datos provenientes de redes sociales en internet, se miden patrones estadísticos de comportamiento social, y se desarrollan métodos cuantitativos, modelos y métricas para el estudio de sistemas complejos tecnológico-sociales.

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Mui, Lik. "Computational models of trust and reputation : agents, evolutionary games, and social networks." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/87343.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2003. Includes bibliographical references (leaves [131]-139). Many recent studies of trust and reputation are made in the context of commercial reputation or rating systems for online communities. Most of these systems have been constructed without a formal rating model or much regard for our sociological understanding of these concepts. We first provide a critical overview of the state of research on trust and reputation. We then propose a formal quantitative model for the rating process. Based on this model, we formulate two personalized rating schemes and demonstrate their effectiveness at inferring trust experimentally using a simulated dataset and a real world movie-rating dataset. Our experiments show that the popular global rating scheme widely used in commercial electronic communities is inferior to our personalized rating schemes when sufficient ratings among members are available. The level of sufficiency is then discussed. In comparison with other models of reputation, we quantitatively show that our framework provides significantly better estimations of reputation. "Better" is discussed with respect to a rating process and specific games as defined in this work. Secondly, we propose a mathematical framework for modeling trust and reputation that is rooted in findings from the social sciences. In particular, our framework makes explicit the importance of social information (i.e., indirect channels of inference) in aiding members of a social network choose whom they want to partner with or to avoid. Rating systems that make use of such indirect channels of inference are necessarily personalized in nature, catering to the individual context of the rater. Finally, we have extended our trust and reputation framework toward addressing a fundamental problem for social science and biology: evolution of cooperation. (cont.) We show that by providing an indirect inference mechanism for the propagation of trust and reputation, cooperation among selfish agents can be explained for a set of game theoretic simulations. For these simulations in particular, our proposal is shown to have provided more cooperative agent communities than existing schemes are able to. by Lik Mui. Ph.D.

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Yang, Guoli. "Learning in adaptive networks : analytical and computational approaches." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/20956.

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The dynamics on networks and the dynamics of networks are usually entangled with each other in many highly connected systems, where the former means the evolution of state and the latter means the adaptation of structure. In this thesis, we will study the coupled dynamics through analytical and computational approaches, where the adaptive networks are driven by learning of various complexities. Firstly, we investigate information diffusion on networks through an adaptive voter model, where two opinions are competing for the dominance. Two types of dynamics facilitate the agreement between neighbours: one is pairwise imitation and the other is link rewiring. As the rewiring strength increases, the network of voters will transform from consensus to fragmentation. By exploring various strategies for structure adaptation and state evolution, our results suggest that network configuration is highly influenced by range-based rewiring and biased imitation. In particular, some approximation techniques are proposed to capture the dynamics analytically through moment-closure differential equations. Secondly, we study an evolutionary model under the framework of natural selection. In a structured community made up of cooperators and cheaters (or defectors), a new-born player will adopt a strategy and reorganise its neighbourhood based on social inheritance. Starting from a cooperative population, an invading cheater may spread in the population occasionally leading to the collapse of cooperation. Such a collapse unfolds rapidly with the change of external conditions, bearing the traits of a critical transition. In order to detect the risk of invasions, some indicators based on population composition and network structure are proposed to signal the fragility of communities. Through the analyses of consistency and accuracy, our results suggest possible avenues for detecting the loss of cooperation in evolving networks. Lastly, we incorporate distributed learning into adaptive agents coordination, which emerges as a consequence of rational individual behaviours. A generic framework of work-learn-adapt (WLA) is proposed to foster the success of agents organisation. To gain higher organisation performance, the division of labour is achieved by a series of events of state evolution and structure adaptation. Importantly, agents are able to adjust their states and structures through quantitative information obtained from distributed learning. The adaptive networks driven by explicit learning pave the way for a better understanding of intelligent organisations in real world.

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Kuhlman, Christopher J. "High Performance Computational Social Science Modeling of Networked Populations." Diss., Virginia Tech, 2013. http://hdl.handle.net/10919/51175.

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Dynamics of social processes in populations, such as the spread of emotions, influence, opinions, and mass movements (often referred to individually and collectively as contagions), are increasingly studied because of their economic, social, and political impacts. Moreover, multiple contagions may interact and hence studying their simultaneous evolution is important. Within the context of social media, large datasets involving many tens of millions of people are leading to new insights into human behavior, and these datasets continue to grow in size. Through social media, contagions can readily cross national boundaries, as evidenced by the 2011 Arab Spring. These and other observations guide our work. Our goal is to study contagion processes at scale with an approach that permits intricate descriptions of interactions among members of a population. Our contributions are a modeling environment to perform these computations and a set of approaches to predict contagion spread size and to block the spread of contagions. Since we represent populations as networks, we also provide insights into network structure effects, and present and analyze a new model of contagion dynamics that represents a person\'s behavior in repeatedly joining and withdrawing from collective action. We study variants of problems for different classes of social contagions, including those known as simple and complex contagions. Ph. D.

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Khan, Pour Hamed. "Computational Approaches for Analyzing Social Support in Online Health Communities." Thesis, University of North Texas, 2018. https://digital.library.unt.edu/ark:/67531/metadc1157594/.

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Online health communities (OHCs) have become a medium for patients to share their personal experiences and interact with peers on topics related to a disease, medication, side effects, and therapeutic processes. Many studies show that using OHCs regularly decreases mortality and improves patients mental health. As a result of their benefits, OHCs are a popular place for patients to refer to, especially patients with a severe disease, and to receive emotional and informational support. The main reasons for developing OHCs are to present valid and high-quality information and to understand the mechanism of social support in changing patients' mental health. Given the purpose of OHC moderators for developing OHCs applications and the purpose of patients for using OHCs, there is no facility, feature, or sub-application in OHCs to satisfy patient and moderator goals. OHCs are only equipped with a primary search engine that is a keyword-based search tool. In other words, if a patient wants to obtain information about a side-effect, he/she needs to browse many threads in the hope that he/she can find several related comments. In the same way, OHC moderators cannot browse all information which is exchanged among patients to validate their accuracy. Thus, it is critical for OHCs to be equipped with computational tools which are supported by several sophisticated computational models that provide moderators and patients with the collection of messages that they need for making decisions or predictions. We present multiple computational models to alleviate the problem of OHCs in providing specific types of messages in response to the specific moderator and patient needs. Specifically, we focused on proposing computational models for the following tasks: identifying emotional support, which presents OHCs moderators, psychologists, and sociologists with insightful views on the emotional states of individuals and groups, and identifying informational support, which provides patients with an efficient and effective tool for accessing the best-fit messages from a huge amount of patient posts to satisfy their information needs, as well as provides OHC moderators, health-practitioners, nurses, and doctors with an insightful view about the current discussion under the topics of side-effects and therapeutic processes, giving them an opportunity to monitor and validate the exchange of information in OHCs. We proposed hybrid models that combine high-level, abstract features extracted from convolutional neural networks with lexicon-based features and features extracted from long short-term memory networks to capture the semantics of the data. We show that our models, with and without lexicon-based features, outperform strong baselines.

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Rossi, Maria. "Graph Mining for Influence Maximization in Social Networks." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLX083/document.

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La science moderne des graphes est apparue ces dernières années comme un domaine d'intérêt et a apporté des progrès significatifs à notre connaissance des réseaux. Jusqu'à récemment, les algorithmes d'exploration de données existants étaient destinés à des données structurées / relationnelles, alors que de nombreux ensembles de données nécessitent une représentation graphique, comme les réseaux sociaux, les réseaux générés par des données textuelles, les structures protéiques 3D ou encore les composés chimiques. Il est donc crucial de pouvoir extraire des informations pertinantes à partir de ce type de données et, pour ce faire, les méthodes d'extraction et d'analyse des graphiques ont été prouvées essentielles.L'objectif de cette thèse est d'étudier les problèmes dans le domaine de la fouille de graphes axés en particulier sur la conception de nouveaux algorithmes et d'outils liés à la diffusion d'informations et plus spécifiquement sur la façon de localiser des entités influentes dans des réseaux réels. Cette tâche est cruciale dans de nombreuses applications telles que la diffusion de l'information, les contrôles épidémiologiques et le marketing viral.Dans la première partie de la thèse, nous avons étudié les processus de diffusion dans les réseaux sociaux ciblant la recherche de caractéristiques topologiques classant les entités du réseau en fonction de leurs capacités influentes. Nous nous sommes spécifiquement concentrés sur la décomposition K-truss qui est une extension de la décomposition k-core. On a montré que les noeuds qui appartiennent au sous-graphe induit par le maximal K-truss présenteront de meilleurs proprietés de propagation par rapport aux critères de référence. De tels épandeurs ont la capacité non seulement d'influencer une plus grande partie du réseau au cours des premières étapes d'un processus d'étalement, mais aussi de contaminer une plus grande partie des noeuds.Dans la deuxième partie de la thèse, nous nous sommes concentrés sur l'identification d'un groupe de noeuds qui, en agissant ensemble, maximisent le nombre attendu de nœuds influencés à la fin du processus de propagation, formellement appelé Influence Maximization (IM). Le problème IM étant NP-hard, il existe des algorithmes efficaces garantissant l’approximation de ses solutions. Comme ces garanties proposent une approximation gloutonne qui est coûteuse en termes de temps de calcul, nous avons proposé l'algorithme MATI qui réussit à localiser le groupe d'utilisateurs qui maximise l'influence, tout en étant évolutif. L'algorithme profite des chemins possibles créés dans le voisinage de chaque nœud et précalcule l'influence potentielle de chaque nœud permettant ainsi de produire des résultats concurrentiels, comparés à ceux des algorithmes classiques.Finallement, nous étudions le point de vue de la confidentialité quant au partage de ces bons indicateurs d’influence dans un réseau social. Nous nous sommes concentrés sur la conception d'un algorithme efficace, correct, sécurisé et de protection de la vie privée, qui résout le problème du calcul de la métrique k-core qui mesure l'influence de chaque noeud du réseau. Nous avons spécifiquement adopté une approche de décentralisation dans laquelle le réseau social est considéré comme un système Peer-to-peer (P2P). L'algorithme est construit de telle sorte qu'il ne devrait pas être possible pour un nœud de reconstituer partiellement ou entièrement le graphe en utilisant les informations obtiennues lors de son exécution. Notre contribution est un algorithme incrémental qui résout efficacement le problème de maintenance de core en P2P tout en limitant le nombre de messages échangés et les calculs. Nous fournissons également une étude de sécurité et de confidentialité de la solution concernant la désanonymisation des réseaux, nous montrons ainsi la rélation avec les strategies d’attaque précédemment definies tout en discutant les contres-mesures adaptés Modern science of graphs has emerged the last few years as a field of interest and has been bringing significant advances to our knowledge about networks. Until recently the existing data mining algorithms were destined for structured/relational data while many datasets exist that require graph representation such as social networks, networks generated by textual data, 3D protein structures and chemical compounds. It has become therefore of crucial importance to be able to extract meaningful information from that kind of data and towards this end graph mining and analysis methods have been proven essential. The goal of this thesis is to study problems in the area of graph mining focusing especially on designing new algorithms and tools related to information spreading and specifically on how to locate influential entities in real-world networks. This task is crucial in many applications such as information diffusion, epidemic control and viral marketing. In the first part of the thesis, we have studied spreading processes in social networks focusing on finding topological characteristics that rank entities in the network based on their influential capabilities. We have specifically focused on the K-truss decomposition which is an extension of the core decomposition of the graph. Extensive experimental analysis showed that the nodes that belong to the maximal K-truss subgraph show a better spreading behavior when compared to baseline criteria. Such spreaders can influence a greater part of the network during the first steps of a spreading process but also the total fraction of the influenced nodes at the end of the epidemic is greater. We have also observed that node members of such dense subgraphs are those achieving the optimal spreading in the network.In the second part of the thesis, we focused on identifying a group of nodes that by acting all together maximize the expected number of influenced nodes at the end of the spreading process, formally called Influence Maximization (IM). The IM problem is actually NP-hard though there exist approximation guarantees for efficient algorithms that can solve the problem while obtaining a solution within the 63% of optimal classes of models. As those guarantees propose a greedy approximation which is computationally expensive especially for large graphs, we proposed the MATI algorithm which succeeds in locating the group of users that maximize the influence while also being scalable. The algorithm takes advantage the possible paths created in each node’s neighborhood to precalculate each node’s potential influence and produces competitive results in quality compared to those of baseline algorithms such as the Greedy, LDAG and SimPath. In the last part of the thesis, we study the privacy point of view of sharing such metrics that are good influential indicators in a social network. We have focused on designing an algorithm that addresses the problem of computing through an efficient, correct, secure, and privacy-preserving algorithm the k-core metric which measures the influence of each node of the network. We have specifically adopted a decentralization approach where the social network is considered as a Peer-to-peer (P2P) system. The algorithm is built based on the constraint that it should not be possible for a node to reconstruct partially or entirely the graph using the information they obtain during its execution. While a distributed algorithm that computes the nodes’ coreness is already proposed, dynamic networks are not taken into account. Our main contribution is an incremental algorithm that efficiently solves the core maintenance problem in P2P while limiting the number of messages exchanged and computations. We provide a security and privacy analysis of the solution regarding network de-anonimization and show how it relates to previously defined attacks models and discuss countermeasures

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Shahrezaye, Morteza [Verfasser], Simon [Akademischer Betreuer] Hegelich, Jürgen [Gutachter] Pfeffer, and Simon [Gutachter] Hegelich. "Understanding big social networks: Applied methods for computational social science / Morteza Shahrezaye ; Gutachter: Jürgen Pfeffer, Simon Hegelich ; Betreuer: Simon Hegelich." München : Universitätsbibliothek der TU München, 2019. http://d-nb.info/1204562296/34.

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Ek, Adam. "Extracting social networks from fiction : Imaginary and invisible friends: Investigating the social world of imaginary friends." Thesis, Stockholms universitet, Institutionen för lingvistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-145659.

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This thesis develops an approach to extract the social relation between characters in literary text to create a social network. The approach uses co-occurrences of named entities, keywords associated with the named entities, and the dependency relations that exist between the named entities to construct the network. Literary texts contain a large amount of pronouns to represent the named entities, to resolve the antecedents of pronouns, a pronoun resolution system is implemented based on a standard pronoun resolution algorithm. The results indicate that the pronoun resolution system finds the correct named entity in 60,4\% of all cases. The social network is evaluated by comparing character importance rankings based on graph properties with an independently human generated importance rankings. The generated social networks correlate moderately to strongly with the independent character ranking.

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Joseph, Kenneth. "New Methods for Large-Scale Analyses of Social Identities and Stereotypes." Research Showcase @ CMU, 2016. http://repository.cmu.edu/dissertations/690.

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Social identities, the labels we use to describe ourselves and others, carry with them stereotypes that have significant impacts on our social lives. Our stereotypes, sometimes without us knowing, guide our decisions on whom to talk to and whom to stay away from, whom to befriend and whom to bully, whom to treat with reverence and whom to view with disgust. Despite these impacts of identities and stereotypes on our lives, existing methods used to understand them are lacking. In this thesis, I first develop three novel computational tools that further our ability to test and utilize existing social theory on identity and stereotypes. These tools include a method to extract identities from Twitter data, a method to infer affective stereotypes from newspaper data and a method to infer both affective and semantic stereotypes from Twitter data. Case studies using these methods provide insights into Twitter data relevant to the Eric Garner and Michael Brown tragedies and both Twitter and newspaper data from the “Arab Spring”. Results from these case studies motivate the need for not only new methods for existing theory, but new social theory as well. To this end, I develop a new sociotheoretic model of identity labeling - how we choose which label to apply to others in a particular situation. The model combines data, methods and theory from the social sciences and machine learning, providing an important example of the surprisingly rich interconnections between these fields.

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Yan, Chang. "A computational game-theoretic study of reputation." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:e6acb250-efb8-410b-86dd-9e3e85b427b6.

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As societies become increasingly connected thanks to advancing technologies and the Internet in particular, individuals and organizations (i.e. agents hereafter) engage in innumerable interaction and face constantly the possibilities thereof. Such unprecedented connectivity offers opportunities through which social and economic benefits are realised and disseminated. Nonetheless, risky and damaging interaction abound. To promote beneficial relationships and to deter adverse outcomes, agents adopt different means and resources. This thesis focuses on reputation as a crucial mechanism for promoting positive interaction, and examines the topic from game-theoretic perspective using computational methods. First, we investigate the design of reputation systems by incorporating economic incentives into algorithm design. Focusing on ubiquitous user-generated ratings on the Internet, we propose a truthful reputation mechanism that not only enforces honest reporting from individual raters but also takes into account their personal preferences. The mechanism is constructed using a blend of Bayesian Truth Serum and SimRank algorithms, both specifically adapted for our use case of online ratings. We show that the resulting mechanism is Bayesian incentive compatible and is computable in polynomial time. In addition, the mechanism is shown to be resistant to common manipulations on the Internet such as uniform fake ratings and targeted collusions. Lastly, we discuss detailed considerations for implementing the mechanism in practice. Second, we investigate experimentally the relative importance of reputational and social knowledge in sustaining cooperation in dynamic networks. In our experiments, U.S-based subjects play a repeated game where, in each round, an endogenous network is formed among a group of 13 players and each player chooses a cooperative or non-cooperative action that applies to all her connections. We vary the availability of reputational and social knowledge to subjects in 4 treatments. At the aggregate level, we find that reputational knowledge is of first-order importance for supporting cooperation, while social knowledge plays a complementary role only when reputational knowledge is available. Further community-level analysis reveals that reputational knowledge leads to the emergence of highly cooperative hubs, and a dense and cluster network, while social knowledge enhances cooperation by forming a large, dense and clustered community of cooperators who exclude outsiders through link removals and link refusals. At the individual level, reputational knowledge proves essential for the emergence of network structural characteristics that are associated with cooperative actions. In contrast, in treatments without reputational information, none of the network metrics is predicative of subjects' choices of action. Furthermore, we present UbiquityLab, a pioneering online platform for conducting real-time interactive experiments for game-theoretic studies. UbiquityLab supports both synchronous and asynchronous game models, and allows for complex and customisable interaction between subjects. It offers both back-end and front-end infrastructure with a modularised design to enable rapid development and streamlined operation. For in- stance, in synchronous mode, all per-stage and inter-stage logic are fully encapsulated by a thin server-side module, while a suite of client-side components eases the creation of game interface. The platform features a robust messaging protocol, such that player connection and game states are restored automatically upon networking errors and dropped out subjects are seamlessly substituted by customisable program players. Online experiments enjoy clear advantages over lab equivalents as they benefit from low operation cost, efficient execution, large and diverse subject pools, etc. UbiquityLab aims to promote online experiments as an emerging research methodology in experimental economics by bringing its benefits to other researchers.

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Shabut, Antesar R. M. "Trust Computational Models for Mobile Ad Hoc Networks. Recommendation Based Trustworthiness Evaluation using Multidimensional Metrics to Secure Routing Protocol in Mobile Ad Hoc Networks." Thesis, University of Bradford, 2015. http://hdl.handle.net/10454/7501.

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Distributed systems like e-commerce and e-market places, peer-to-peer networks, social networks, and mobile ad hoc networks require cooperation among the participating entities to guarantee the formation and sustained existence of network services. The reliability of interactions among anonymous entities is a significant issue in such environments. The distributed entities establish connections to interact with others, which may include selfish and misbehaving entities and result in bad experiences. Therefore, trustworthiness evaluation using trust management techniques has become a significant issue in securing these environments to allow entities decide on the reliability and trustworthiness of other entities, besides it helps coping with defection problems and stimulating entities to cooperate. Recent models on evaluating trustworthiness in distributed systems have heavily focused on assessing trustworthiness of entities and isolate misbehaviours based on single trust metrics. Less effort has been put on the investigation of the subjective nature and differences in the way trustworthiness is perceived to produce a composite multidimensional trust metrics to overcome the limitation of considering single trust metric. In the light of this context, this thesis concerns the evaluation of entities’ trustworthiness by the design and investigation of trust metrics that are computed using multiple properties of trust and considering environment. Based on the concept of probabilistic theory of trust management technique, this thesis models trust systems and designs cooperation techniques to evaluate trustworthiness in mobile ad hoc networks (MANETs). A recommendation based trust model with multi-parameters filtering algorithm, and multidimensional metric based on social and QoS trust model are proposed to secure MANETs. Effectiveness of each of these models in evaluating trustworthiness and discovering misbehaving nodes prior to interactions, as well as their influence on the network performance has been investigated. The results of investigating both the trustworthiness evaluation and the network performance are promising. Ministry of Higher Education in Libya and the Libyan Cultural Attaché bureau in London

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Shabut, Antesar Ramadan M. "Trust computational models for mobile ad hoc networks : recommendation based trustworthiness evaluation using multidimensional metrics to secure routing protocol in mobile ad hoc networks." Thesis, University of Bradford, 2015. http://hdl.handle.net/10454/7501.

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Distributed systems like e-commerce and e-market places, peer-to-peer networks, social networks, and mobile ad hoc networks require cooperation among the participating entities to guarantee the formation and sustained existence of network services. The reliability of interactions among anonymous entities is a significant issue in such environments. The distributed entities establish connections to interact with others, which may include selfish and misbehaving entities and result in bad experiences. Therefore, trustworthiness evaluation using trust management techniques has become a significant issue in securing these environments to allow entities decide on the reliability and trustworthiness of other entities, besides it helps coping with defection problems and stimulating entities to cooperate. Recent models on evaluating trustworthiness in distributed systems have heavily focused on assessing trustworthiness of entities and isolate misbehaviours based on single trust metrics. Less effort has been put on the investigation of the subjective nature and differences in the way trustworthiness is perceived to produce a composite multidimensional trust metrics to overcome the limitation of considering single trust metric. In the light of this context, this thesis concerns the evaluation of entities’ trustworthiness by the design and investigation of trust metrics that are computed using multiple properties of trust and considering environment. Based on the concept of probabilistic theory of trust management technique, this thesis models trust systems and designs cooperation techniques to evaluate trustworthiness in mobile ad hoc networks (MANETs). A recommendation based trust model with multi-parameters filtering algorithm, and multidimensional metric based on social and QoS trust model are proposed to secure MANETs. Effectiveness of each of these models in evaluating trustworthiness and discovering misbehaving nodes prior to interactions, as well as their influence on the network performance has been investigated. The results of investigating both the trustworthiness evaluation and the network performance are promising.

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Alqithami, Saad. "Network Organization Paradigm." OpenSIUC, 2016. https://opensiuc.lib.siu.edu/dissertations/1293.

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In a complex adaptive system, diverse agents perform various actions without adherence to a predefined structure. The achievement of collaborative actions will be the result of continual interactions among them that shape a dynamic network. Agents may form an ad hoc organization based on the dynamic network of interactions for the purpose of achieving a long-term objective, which we termed a Network Organization (NO). Fervent and agile communication on social networking sites provides opportunities for potential issues to trigger individuals into individual actions as well as the attraction and mobilization of like-minded individuals into an NO that is both physically and virtually emergent. Examples are the rapid pace of Arab Spring proliferation and the diffusion rate of the Occupy Movement. We are motivated by a spontaneously formed NO as well as the quality of plasticity that enables the organization to change rapidly to describe an NO. Thus, we present a paradigm that serves as a reference model for organizations of socially networked individuals. This paradigm suggests modular components that can be combined to form an ad hoc network organization of agents. We touch on how this model accounts for external change in an environment through internal adjustment. For the predominant influences of the network substrate in an NO, multiple effects of it have an impact on the NO behaviors and directions. We envisioned several dimensions of such effects to include synergy, social capital, externality, influence, etc. A special focus in this work is measuring synergy and social capital as two predominant network effects. Synergy is perceived as different modalities of compatibility among agents when performing a set of coherent and correspondingly different actions. When agents are under no structural obligation to contribute, synergy is quantified through multiple forms of serendipitous agent chosen benevolence among them. The approach is to measure four types of benevolence and the pursuant synergies stemming from agent interactions. Social capital is another effect of networking that describes the accumulation of positive values of social flow and perceived trust plus abundance of communication over the common topic of NO. We provide measurement of social capital based on an agents’ expected benevolence. We examine those two effects in two different case studies — one case of a virtual organization and another of a real world terrorist organization — that best illustrate the main tenets of our conceptualization.

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Watts, Jameson K. M., and Jameson K. M. Watts. "Language Consistency and Exchange: Market Reactions to Change in the Distribution of Field-level Information." Diss., The University of Arizona, 2015. http://hdl.handle.net/10150/556000.

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Markets are fluid. Over time, the dominant designs, processes and paradigms that define an industry invariably succumb to productive innovation or changes in fashion (Arthur, 2009; Schumpeter, 1942; Simmel, 1957). Take for example the recent upheaval of the cell phone market following Apple's release of the iPhone. When it was introduced in 2007, one could clearly differentiate Apple's product from all others; however, subsequent imitation of the iPhone produced a market in which nearly all cell phones look (and perform) alike. The iPhone was a harbinger of the new dominant design. These cycles of innovation and fashion are not limited to consumer markets. Business markets (often defined by longer term inter-firm relationships) are subject to similar transformations. For example, current practices in the biotechnology industry are quite distinct from those accompanying its emergence from university labs in the second half of the 20th century (Powell et al., 2005). Technologies that were once viewed as radical have undergone a process of legitimation and integration into mainstream healthcare delivery systems. Practices that were dominant in the 1980's gave way to newer business models in the 1990's and feedback from down-stream providers changed the way drugs were delivered to patients (Wolff, 2001).During periods of transition, market actors face great difficulty anticipating reactions to their behavior (practices, products, etc.). How they deal with this uncertainty is an interminable source of academic inquiry in the social sciences (see e.g. Alderson, 1965; Simon, 1957; Thompson, 1967) and, in a broad sense, it is the primary concern of the current work as well. However, I am focused specifically on the turmoil caused by transitions in technology, taste and attention over time--the disagreements which occur as market actors collectively shift their practices from one paradigm to the next (Powell and Colyvas, 2008). If innovations are assumed to arise locally and diffuse gradually (see e.g. Bass, 1969; Rogers, 2002), then transient differences in knowledge are a natural outcome. Those closest to, or most interested in an innovation will have greater knowledge than those furthest away or less involved. Thus, for a period following some shift in technology, taste or attention, market participants will vary in their knowledge and interpretation of the change. In the following chapters, I investigate the ramifications of this sort of knowledge heterogeneity on the exchange behavior and subsequent performance of market participants. It is the central argument of this thesis that this heterogeneity affects exchange by both limiting coordination and increasing quality uncertainty. The details of this argument are fleshed out in Chapters 1, 2 and 3 (summarized below), which build upon each other in a progression from abstract, to descriptive to specific tests of theory. However, each can also stand by itself as an independent examination of the knowledge-exchange relationship. The final chapter synthesizes my findings and highlights some implications for practitioners and further research. In Chapter 1, I review the history and development of Alderson's (1965) 'law of exchange' in the marketing literature and propose an extension based on insights from information theory. A concept called market entropy is introduced to describe the distribution of knowledge in a field and propositions are offered to explain the exchange behavior expected when this distribution changes. Chapter 2 investigates knowledge heterogeneity through its relation with written language. Drawing on social-constructionist theories of classification (Goldberg, 2012) and insights from research on the legitimation process (Powell and Colyvas, 2008), I argue for a measure of field-level consensus based on changes in the frequency distribution of descriptive words over time. This measure is operationalized using eleven years of trade journal articles from the biotech industry and is shown to support the propositions offered in Chapter 1. Chapter 3 builds on the arguments and evidence developed in Chapters 1 and 2 to test theory on the structural advantages of a firm's position in a network of strategic alliances. Prior work has documented returns to network centrality based on the premise that central firms have greater and more timely access to information about industry developments (Powell et al., 1996, 1999). However, other research claims that benefits to centrality accrue based on the signal that such a position provides about an actor's underlying quality (Malter, 2014; Podolny, 1993, 2005). I investigate this tension in the literature and offer new insights based on interactions between network position and the measure developed in Chapter 2.

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Rocha, Luis E. C. "Exploring patterns of empirical networks." Doctoral thesis, Umeå universitet, Institutionen för fysik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-46588.

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We are constantly struggling to understand how nature works, trying to identify recurrent events and looking for analogies and relations between objects or individuals. Knowing patterns of behavior is powerful and fundamental for survival of any species. In this thesis, datasets of diverse systems related to transportation, economics, sexual and social contacts, are characterized by using the formalisms of time series and network theory. Part of the results consists on the collection and analyzes of original network data, the rest focuses on the simulation of dynamical processes on these networks and to study how they are affected by the particular structures. The majority of the thesis is about temporal networks, i.e. networks whose structure changes in time. The new temporal dimension reveals structural dynamical properties that help to understand the feedback mechanisms responsible to make the network structure to adapt and to understand the emergence and inhibition of diverse phenomena in dynamic systems, as epidemics in sexual and contact networks. Vi är ständigt kämpar för att förstå hur naturen fungerar, försöker identifier återkommande evenemang och söker analogier och relationer mellan objekt eller individer. Veta beteendemönster är kraftfull och grundläggande för överlevnad av arter. I denna avhandling, dataset av olika system i samband med transporter är ekonomi, sexuella och sociala kontakter, som kännetecknas av att använda formalismer av tidsserier och nätverk teori. En del av resultatet utgörs av insamling och analys av ursprungliga nätdata, fokuserar resten på simulering av dynamiska processer i dessa nätverk och att studera hur de påverkas av de särskilda strukturer. Huvuddelen av avhandlingen handlar om tidsmässiga nät, i.e. nät vars struktur förändringar i tid. Den nya tidsdimensionen avslöjar strukturella dynamiska egenskaper som hjälper till att förstå den feedback mekanismer som ansvarar för att göra nätverksstruktur att anpassa sig och förstå uppkomsten och hämning av olika företeelser i dynamiska system, epidemier i sexuella och kontaktnät. Constantemente nos esforçamos para entender como a natureza funciona, tentando identificar eventos recorrentes e procurando por analogias e relações entre objetos ou indivíduos. Conhecer padrões de comportamento é algo poderoso e fundamental para a sobrevivência de qualquer espécie. Nesta tese, dados de sistemas diversos, relacionados a transporte, economia, contatos sexuais e sociais, são caracterizados usando o formalismo de séries temporais e teoria de redes. Uma parte dos resultados consiste na coleta e análise de dados de redes originais, a outra parte concentra-se na simulação de processos dinâmicos nessas redes e no estudo de como esses processos são afetados por determinadas estruturas. A maior parte da tese é sobre redes temporais, ou seja, redes cuja estrutura varia no tempo. A nova dimensão temporal revela propriedades estruturais dinâmicas que contribuem para o entendimento dos mecanismos de resposta responsáveis pela adaptação da rede, e para o entendimento da emergência e inibição de fenômenos diversos em sistemas dinâmicos, como epidemias em redes sexuais e de contato pessoal.

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Etling, Bruce. "Network structure, brokerage, and framing : how the internet and social media facilitate high-risk collective action." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:2c08ba3d-2eb0-41ee-ace5-cb1f893c951e.

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This thesis investigates the role of network structure, brokerage, and framing in high-risk collective action. I use the protest movement that emerged in Russia following falsified national elections in 2011 and 2012 as an empirical case study. I draw on a unique dataset of nearly 30,000 online documents and the linking structure of over 3,500 Russian Web sites. I employ a range of computational social science methods, including Exponential Random Graph Modeling, an advanced statistical model for social networks, social network analysis, machine learning, and latent semantic analysis. I address three research questions in this thesis. The first asks if a protest network challenging a hybrid regime will have a polycentric or hierarchical structure, and if that structure changes over time. Polycentric networks are conducive to high-risk collective action and are robust to the targeted removal of key nodes, while hierarchical networks can more easily mobilize protesters and spread information. I find that the Russian protest network has a polycentric structure only at the beginning of the protests, and moves towards a less effective hierarchical structure as the movement loses popular support. The second research question seeks to understand if brokered text is actually novel, and if that text is more novel in polycentric networks than in hierarchical ones. Brokers are the individuals or nodes in a network that connect disparate groups through weak ties and close structural holes. Brokers are advantageous because they have access to and spread novel information. I find that the text among nodes in brokered relationships is indeed novel, but that information novelty decreases when networks have a hierarchical structure. The last research question asks if a protest movement in a high-risk political setting can be more successful than the government at spreading its preferred frames, and within such a movement, whether moderate or extremist framing is more prevalent. I find that the opposition is far more effective than the government in spreading its frames, even when the government organizes massive counter protests. Within the movement, moderates are more likely to have their framing adopted online than extremists, unless violence occurs at protests. The findings suggest that movements should build flatter, more diffuse networks by ensuring that brokers tie together diverse protest constituencies. The findings also provide evidence against those who claim that authoritarian governments are more effective in shaping online discourse than oppositional movements, and also suggest that movements should advance moderate framing in order to attract a wider base of support among the general population.

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Srinivasan, Ramprakash. "Computational Models of the Production and Perception of Facial Expressions." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1531239299392184.

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You, Bo. "Hub-Network for Distance Computation in Large Social Networks." Kent State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=kent1412601464.

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Shaikh, Sajid S. "COMPUTATION IN SOCIAL NETWORKS." Kent State University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=kent1185560088.

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Shaikh, Sajid S. "Computations in social network." [Kent, Ohio] : Kent State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=kent1185560088.

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Mignot, Sylvain. "Négocier ou enchérir, l’influence des mécanismes de vente : le cas du marché aux poissons de Boulogne-sur-Mer." Thesis, Paris 2, 2012. http://www.theses.fr/2012PA020101/document.

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Le marché aux poissons de Boulogne-sur-Mer se caractérise par l’organisation singulière de son système de vente. En effet, sur celui-ci, les acheteurs et les vendeurs peuvent choisir chaque jour de recourir à un mécanisme d’enchères ou à un marché de gré à gré (voire à ces deux possibilités en même temps), pour commercer entre eux. La coexistence de ces deux systèmes de vente est stable dans le temps, chacun d’entre eux représentant approximativement la moitié des quantités échangées. Cette singularité économique conduit à s’interroger sur les conditions nécessaires à l’émergence et à la stabilité de cette coexistence. Pourquoi les agents ne s’accordent-ils tous pas pour un unique mécanisme de transaction comme dans la majorité des marchés? pourquoi observe-t-on une si grande volatilité dans les choix individuels de marché? Afin de comprendre les conditions nécessaires à cette coexistence de mécanismes de marché, la présente thèse se déclinera comme suit. La première partie sera dédiée à l’étude empirique des transactions journalières ayant lieu sur chacun des deux sous-marchés. Nous commençons par une analyse statistique et économétrique afin d’extraire les faits stylisés représentatifs des propriétés du marché et de ses acteurs, avant de procéder à une analyse des réseaux sociaux existants sur ce marché,visant à déterminer l’influence des interactions dans la prise de décision. Fort de ces résultats, nous construisons des modèles informatiques multi-agents, capables de reproduire les comportements observés au niveau individuel, et, au travers ceux-ci,le comportement du marché lui-même au niveau agrégé Should I buy or should I bid ? The influence of market mechanism : the case of Boulogne-Sur-Mer fish market. The Boulogne-sur-Mer fish market is organized in a very specific way. Each day buyers and sellers can choose to use either an auction mechanism, a negotiated market, or evenboth, in order to sell and buy goods.A stunning fact observed is the stable coexistence of those two sub-markets throughout time, with no convergence of agents toward one of them, each one accounting for roughly half of the exchanged quantities.The present thesis aims at discovering the necessary conditions of the emergence andstability of such a coexistence.To do it, we will begin with an empirical study of daily transactions that have occurred on this market for a few years. We begin with a statistical and econometric study to extract the main stylized facts of this market, then we study the social networks influencing the outcomes. Once those facts determined, we build agent-based computational models able to reproduce the individual behaviours of agents, and through these, the emergence of the market’sbehaviour itself

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Obradović, Darko [Verfasser]. "Computational Social Network Analysis of Authority in the Blogosphere / Darko Obradović." München : Verlag Dr. Hut, 2012. http://d-nb.info/1028786891/34.

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Lospinoso, Joshua Alfred. "Statistical models for social network dynamics." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:d5ed9b9c-020c-4379-a5f2-cf96439ca37c.

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The study of social network dynamics has become an increasingly important component of many disciplines in the social sciences. In the past decade, statistical models and methods have been proposed which permit researchers to draw statistical inference on these dynamics. This thesis builds on one such family of models, the stochastic actor oriented model (SAOM) proposed by Snijders [2001]. Goodness of fit for SAOMs is an area that is only just beginning to be filled in with appropriate methods. This thesis proposes a Mahalanobis distance based, Monte Carlo goodness of fit test that can depend on arbitrary features of the observed network data and covariates. As remediating poor fit can be a difficult process, a modified model distance (MMD) estimator is devised that can help researchers to choose among a set of model elaborations. In practice, panel data is typically used to draw SAOM-based inference. This thesis also proposes a score-type test for time heterogeneity between the waves in the panel that is computationally cheap and fits into a convenient, forward model selecting workflow. Next, this thesis proposes a rigorous method for aggregating so-called relational event data (e.g. emails and phone calls) by extending the SAOM family to a family of hidden Markov models that suppose a latent social network is driving the observed relational events. Finally, this thesis proposes a measurement model for SAOMs inspired by error-in-variables (EiV) models employed in an array of disciplines. Like the relational event aggregation model, the measurement model is a hidden Markov model extension to the SAOM family. These models allow the researcher to specify the form of the mesurement error and buffer against potential attenuating biases and other problems that can arise if the errors are ignored.

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Wilczynski, Anaëlle. "Interaction entre agents modélisée par un réseau social dans des problématiques de choix social computationnel Strategic Voting in a Social Context: Considerate Equilibria Object Allocation via Swaps along a Social Network Local Envy-Freeness in House Allocation Problems Constrained Swap Dynamics over a Social Network in Distributed Resource Reallocation Poll-Confident Voters in Iterative Voting." Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLED073.

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Le choix social repose sur l’étude de la prise de décision collective, où un ensemble d’individus doit convenir d’une solution commune en fonction des préférences de ses membres. Le problème revient à déterminer comment agréger les préférences de différents agents en une décision acceptable pour le groupe. Typiquement, les agents interagissent dans des processus de décision collective, notamment en collaborant ou en échangeant des informations. Il est communément supposé que tout agent est capable d’interagir avec n’importe quel autre. Or, cette hypothèse paraît irréaliste pour de nombreuses situations. On propose de relâcher cette hypothèse en considérant que la possibilité d’interaction est déterminée par un réseau social, représenté par un graphe sur les agents. Dans un tel contexte, on étudie deux problèmes de choix social : le vote stratégique et l’allocation de ressources. L’analyse se concentre sur deux types d’interaction : la collaboration entre les agents, et la collecte d’information. On s’intéresse à l’impact du réseau social, modélisant une possibilité de collaboration entre les agents ou une relation de visibilité, sur la résolution et les solutions de problèmes de vote et d’allocation de ressources. Nos travaux s’inscrivent dans le cadre du choix social computationnel, en utilisant pour ces questions des outils provenant de la théorie des jeux algorithmique et de la théorie de la complexité Social choice is the study of collective decision making, where a set of agents must make a decision over a set of alternatives, according to their preferences. The question relies on how aggregating the preferences of the agents in order to end up with a decision that is commonly acceptable for the group. Typically, agents can interact by collaborating, or exchanging some information. It is usually assumed in computational social choice that every agent is able to interact with any other agent. However, this assumption looks unrealistic in many concrete situations. We propose to relax this assumption by considering that the possibility of interaction is given by a social network, represented by a graph over the agents.In this context, we study two particular problems of computational social choice: strategic voting and resource allocation of indivisible goods. The focus is on two types of interaction: collaboration and information gathering. We explore how the social network,modelingapossibilityofcollaboration or a visibility relation among the agents, can impact the resolution and the solution of voting and resource allocation problems. These questions are addressed via computational social choice by using tools from algorithmic game theory and computational complexity

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Ballester, Pla Coralio. "On Peer Networks and Group Formation." Doctoral thesis, Universitat Autònoma de Barcelona, 2005. http://hdl.handle.net/10803/4064.

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En el artículo "NP-completeness in Hedonic Games", identificamos algunas limitaciones significativas de los modelos estándar de juegos cooperativos: A menudo, es imposible alcanzar una organización estable de una sociedad en una cantidad de tiempo razonable. Las implicaciones básicas de estos resultados son las siguientes, Primero, desde un punto de vista positivo, las sociedades están "condenadas" a evolucionar constantemente, más que a alcanzar un estadio de equilibrio en el corto plazo. Segundo, desde una perspectiva normativa, un hipotético organizador de la sociedad debería tomar en consideración las limitaciones prácticas de tiempo a la hora de implementar un orden social estable. Para obtener nuestros resultados, utilizamos el concepto de NP-completitud, que es un modelo bien establecido de complejidad temporal en Ciencias de la Computación. En concreto, nos concentramos en estabilidad grupal y estabilidad individual en juegos hedónicos. Los juegos hedónicos son una clase simple de juegos cooperativos en los que la utilidad de cada individuo viene totalmente determinada por el grupo laboral al que pertenece. Nuestros resultados referentes a la complejidad, expresados en términos de NP-completitud, cubren un amplio espectro de dominios de las preferencias individuales, incluyendo preferencias estrictas, indiferencias en las preferencias o preferencias libres sobre el tamaño de los grupos. Dichos resultados también se cumplen si nos restringimos al caso en el que el tamaño máximo de los grupos es pequeño (dos o tres jugadores) En el artículo "Who is Who in Networks. Wanted: The Key Player" (junto con Antoni Calvó Armengol e Yves Zenou), analizamos un modelo de efectos de grupo en el que los agentes interactúan en un juego de influencias bilaterales. Los juegos no cooperativos con población finita y utilidades linales-cuadráticas, en los cuales cada jugador decide cuánto esfuerzo ejercer, pueden ser interpretados como juegos en red con complementariedades en los pagos, junto con un componente de susitucion global y uniforme, y un efecto de concavidad propia. Para dichos juegos, la acción de cada jugador en un equilibrio de Nash es proporcional a su centralidad de Bonacich en la red de complementariedades, estableciendo así un puente con la literatura de redes sociales. Dicho vínculo entre Bonacich y Nash implica que el equilibrio agregado aumenta con el tamaño y la densidad de la red. También analizamos una política que consiste en seleccionar al jugador clave, ésto es, el jugador que, una vez eliminado del juego, induce un cambio óptimo en la actividad agregada. Proveemos una caracterización geométrica del jugador clave, identificada con una medida de inter-centralidad, la cual toma en cuenta tanto la centralidad de cada jugador como su contribución a la centralidad de los otros. En el artículo "Optimal Targets in Peer Networks" (junto con Antoni Calvó Armengol e Yves Zenou), nos centramos en las consecuencias y limitaciones prácticas que se derivan del modelo de decisiones sobre delincuencia. Las principales metas que aborda el trabajo son las siguientes. Primero, la elección se extiende el concepto de delincuente clave en una red al de grupo clave. En dicha situación se trata de seleccionar de modo óptimo al conjunto de delincuentes a eliminar/neutralizar, dadas las restricciones presupuestarias para aplicar medidas. Dicho problema presenta una inherente complejidad computacional que solo puede salvarse mediante el uso de procedimientos aproximados, "voraces" o probabilísticos. Por otro lado, tratamos el problema del delincuente clave en el contexto de redes dinámicas, en las que, inicialmente, los individuos deciden acerca de su futuro como delincuentes o como ciudadanos que obtienen un salario fijo en el mercado. En dicha situación, la elección del delincuente clave es más compleja, ya que el objetivo de disminuir la delincuencia debe tener en cuenta los efectos en cadena que pueda traer consigo la desaparición de uno o varios delincuentes. Por último, estudiamos la complejidad computacional del problema de elección óptima y explotamos la propiedad de submodularidad de la intercentralidad de grupo, lo cual nos permite acotar el error relativo de la aproximación basada en un algoritmo voraz. The aim of this thesis work is to contribute to the analysis of the interaction of agents in social networks and groups. In the chapter "NP-completeness in Hedonic Games", we identify some significant limitations in standard models of cooperation in games: It is often impossible to achieve a stable organization of a society in a reasonable amount of time. The main implications of these results are the following. First, from a positive point of view, societies are bound to evolve permanently, rather than reach a steady state configuration rapidly. Second, from a normative perspective, a planner should take into account practical time limitations in order to implement a stable social order. In order to obtain our results, we use the notion of NP-completeness, a well-established model of time complexity in Computer Science. In particular, we concentrate on group stability and individual stability in hedonic games. Hedonic games are a simple class of cooperative games in which each individual's utility is entirely determined by her group. Our complexity results, phrased in terms of NP-completeness, cover a wide spectrum of preference domains, including strict preferences, indifference in preferences or undemanding preferences over sizes of groups. They also hold if we restrict the maximum size of groups to be very small (two or three players). The last two chapters deal with the interaction of agents in the social setting. It focuses on games played by agents who interact among them. The actions of each player generate consequences that spread to all other players throughout a complex pattern of bilateral influences. In "Who is Who in Networks. Wanted: The Key Player" (joint with Antoni Calvó-Armengol and Yves Zenou), we analyze a model peer effects where agents interact in a game of bilateral influences. Finite population non-cooperative games with linear-quadratic utilities, where each player decides how much action she exerts, can be interpreted as a network game with local payoff complementarities, together with a globally uniform payoff substitutability component and an own-concavity effect. For these games, the Nash equilibrium action of each player is proportional to her Bonacich centrality in the network of local complementarities, thus establishing a bridge with the sociology literature on social networks. This Bonacich-Nash linkage implies that aggregate equilibrium increases with network size and density. We then analyze a policy that consists in targeting the key player, that is, the player who, once removed, leads to the optimal change in aggregate activity. We provide a geometric characterization of the key player identified with an inter-centrality measure, which takes into account both a player's centrality and her contribution to the centrality of the others. Finally, in the last chapter, "Optimal Targets in Peer Networks" (joint with Antoni Calvó-Armengol and Yves Zenou), we analyze the previous model in depth and study the properties and the applicability of network design policies. In particular, the key group is the optimal choice for a planner who wishes to maximally reduce aggregate activity. We show that this problem is computationally hard and that a simple greedy algorithm used for maximizing submodular set functions can be used to find an approximation. We also endogeneize the participation in the game and describe some of the properties of the key group. The use of greedy heuristics can be extended to other related problems, like the removal or addition of new links in the network.

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Santos, Francisco C. "Topological evolution: from biological to social networks." Doctoral thesis, Universite Libre de Bruxelles, 2007. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210702.

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ABOUEIMEHRIZI, MOHAMMAD. "Election Control via Social Influence." Doctoral thesis, Gran Sasso Science Institute, 2021. http://hdl.handle.net/20.500.12571/21656.

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In the past, the power of news dissemination was under a few people's control, like newspapers' editors and TV channels. Thanks to social networks, this power is in the hand of everyone now. Social networks became very popular as soon as they were launched, and many societies extensively welcomed them. They have provided an engaging environment so that people can share their moments with their relatives, friends, colleagues, and even their unseen friends (so-called virtual friends) as their `followers.' In this virtual world, people can also share their opinions with their followers by broadcasting a message. Diffusing information and news among the followers will affect them and slightly change their opinions. When a follower is influenced, she may shares/retweets/forwards the message to her own followers and cause more propagation. There are many shreds of evidence that a message shared by few people (even in some cases one person) has been watched by millions of users and went viral. Hence, social media is an inseparable part of our life that can provide many opportunities, e.g., teaching, entertainment, news, and give us the power of sharing our experiences. Researchers have shown that many people prefer to get news from social networks rather than related websites as they are speedy tool to provide news from everywhere. Therefore, social media is considered one of the most effective tools to manipulate the users' opinions, and it is an attractive means of election control for political campaigns/parties/candidates. As a real example, in the 2016 US presidential election, it has been shown that 92% of Americans saw and remembered pro-Trump fake news stories, 23% remembered pro-Clinton false news, and a very high portion of them believed the news. Moreover, the campaigns can use social influence in order to polarize the users such that a voter receives specific messages in support/oppose of a candidate/party and not all possible messages. These activities impair the integrity of the elections and our democracies because people should have access to all reliable news from different perspectives to make a fair judgment. In this thesis, we investigate the computational aspects of this problem and study different manipulators' strategies to understand how they work. Our goal is to prevent malicious activities as they have enough potential to cause drastic consequences for any society. We study different aspects of controlling elections utilizing social influence. First, we consider a multi-winner election control where some parties are running for an election, and more than one candidate will be selected as winners. There is a social network of voters and an attacker trying to bribe some users/voters to start a diffusion process and spread a message among them; her goal is to change the voters' opinion regarding a target party. In the constructive model, the attacker tries to maximize the number of winners in the target party, while in the destructive case, she wants to minimize it. In this model, we present some hardness results, approximation guarantee, and polynomial-time algorithms regarding different structures (e.g., graphs, trees, and arborescent), objective functions, diffusion models (e.g., linear threshold and independent cascade models), and different configurations of influencing voters. Second, we investigate a single-winner election control problem where the attacker does not know the exact voters' preference list; instead, she has/guesses a probability distribution over all candidates for each voter. In this case, we show that the problem is at least as hard to approximate as the Densest-k-subgraph problem, which is hard to approximate for some constant under the exponential time hypothesis. Then we consider a lightly relaxed version and present some hardness and constant factor approximation algorithms for some objective functions regarding both constructive and destructive models. We also examine some real-world social networks and experimentally show that our algorithm works well. Finally, we present a Stackelberg game variation for competitive election control where there are two players called attacker and defender. They have a budget and the number of their seed nodes should not exceed their budget. The attacker plays first and selects a set of seed nodes to start a diffusion and change the voters' opinion. She knows that the defender is aware of everything and plays afterward. When the attacker's diffusion process is finished, the defender selects her seed nodes to cancel the attacker's influence over the infected voters. Indeed, the attacker tries to maximize the number of infected voters after both diffusion processes, while the defender attempts to minimize it. For simplicity, we first investigate the influence maximization model of this problem and then extend it to the election control through social influence for a single-winner election control problem regarding plurality scoring rule under the independent cascade model. We show that the attacker's problem is $Sigma_2^p$-hard when the defender is able to find an optimal strategy. We also show the same hardness result regarding any approximation algorithm. Moreover, we show that the defender's problem is NP-hard to approximate within any factor $alpha geq 1$. Since the problems are inapproximable, we consider a relaxed version in which the defender selects her seed nodes based on a probability distribution over the nodes, and the attacker is aware of the distribution. In the relaxed model, we give a constant-factor approximation algorithm for the attacker's problem. We also simulate our results and show that the attacker can activate many voters even when the defender can find the optimal solution. Moreover, we show that the greedy influence maximization algorithm works very well for the defender.

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Nagurney, Anna, and Dae-Shik Kim. "Parallel Computation of Large-Scale Nonlinear Network Problems in the Social and Economic Sciences." Massachusetts Institute of Technology, Operations Research Center, 1990. http://hdl.handle.net/1721.1/5400.

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In this paper we focus on the parallel computation of large - scale equilibrium and optimization problems arising in the social and economic sciences. In particular, we consider problems which can be visualized and conceptualized as nonlinear network flow problems. The underlying network structure is then exploited in the development of parallel decomposition algorithms. We first consider market equilibrium problems, both dynamic and static, which are formulated as variational inequality problems, and for which we propose parallel decomposition algorithms by time period and by commodity, respectively. We then turn to the parallel computation of large-scale constrained matrix problems which are formulated as optimization problems and discuss the results of parallel decomposition by row/column.

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Riquelme, Csori Fabián. "Structural and computational aspects of simple and influence games." Doctoral thesis, Universitat Politècnica de Catalunya, 2014. http://hdl.handle.net/10803/283144.

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Simple games are a fundamental class of cooperative games. They have a huge relevance in several areas of computer science, social sciences and discrete applied mathematics. The algorithmic and computational complexity aspects of simple games have been gaining notoriety in the recent years. In this thesis we review different computational problems related to properties, parameters, and solution concepts of simple games. We consider different forms of representation of simple games, regular games and weighted games, and we analyze the computational complexity required to transform a game from one representation to another. We also analyze the complexity of several open problems under different forms of representation. In this scenario, we prove that the problem of deciding whether a simple game in minimal winning form is decisive (a problem that is associated to the duality problem of hypergraphs and monotone Boolean functions) can be solved in quasi-polynomial time, and that this problem can be polynomially reduced to the same problem but restricted to regular games in shift-minimal winning form. We also prove that the problem of deciding wheter a regular game is strong in shift-minimal winning form is coNP-complete. Further, for the width, one of the parameters of simple games, we prove that for simple games in minimal winning form it can be computed in polynomial time. Regardless of the form of representation, we also analyze counting and enumeration problems for several subfamilies of these games. We also introduce influence games, which are a new approach to study simple games based on a model of spread of influence in a social network, where influence spreads according to the linear threshold model. We show that influence games capture the whole class of simple games. Moreover, we study for influence games the complexity of the problems related to parameters, properties and solution concepts considered for simple games. We consider extremal cases with respect to demand of influence, and we show that, for these subfamilies, several problems become polynomial. We finish with some applications inspired on influence games. The first set of results concerns to the definition of collective choice models. For mediation systems, several of the problems of properties mentioned above are polynomial-time solvable. For influence systems, we prove that computing the satisfaction (a measure equivalent to the Rae index and similar to the Banzhaf value) is hard unless we consider some restrictions in the model. For OLFM systems, a generalization of OLF systems (van den Brink et al. 2011, 2012) we provide an axiomatization of satisfaction. The second set of results concerns to social network analysis. We define new centrality measures of social networks that we compare on real networks with some classical centrality measures. Los juegos simples son una clase fundamental de juegos cooperativos, que tiene una enorme relevancia en diversas áreas de ciencias de la computación, ciencias sociales y matemáticas discretas aplicadas. En los últimos años, los distintos aspectos algorítmicos y de complejidad computacional de los juegos simples ha ido ganando notoriedad. En esta tesis revisamos los distintos problemas computacionales relacionados con propiedades, parámetros y conceptos de solución de juegos simples. Primero consideramos distintas formas de representación de juegos simples, juegos regulares y juegos de mayoría ponderada, y estudiamos la complejidad computacional requerida para transformar un juego desde una representación a otra. También analizamos la complejidad de varios problemas abiertos bajo diferentes formas de representación. En este sentido, demostramos que el problema de decidir si un juego simple en forma ganadora minimal es decisivo (un problema asociado al problema de dualidad de hipergrafos y funciones booleanas monótonas) puede resolverse en tiempo cuasi-polinomial, y que este problema puede reducirse polinomialmente al mismo problema pero restringido a juegos regulares en forma ganadora shift-minimal. También demostramos que el problema de decidir si un juego regular en forma ganadora shift-minimal es fuerte (strong) es coNP-completo. Adicionalmente, para juegos simples en forma ganadora minimal demostramos que el parámetro de anchura (width) puede computarse en tiempo polinomial. Independientemente de la forma de representación, también estudiamos problemas de enumeración y conteo para varias subfamilias de juegos simples. Luego introducimos los juegos de influencia, un nuevo enfoque para estudiar juegos simples basado en un modelo de dispersión de influencia en redes sociales, donde la influencia se dispersa de acuerdo con el modelo de umbral lineal (linear threshold model). Demostramos que los juegos de influencia abarcan la totalidad de la clase de los juegos simples. Para estos juegos también estudiamos la complejidad de los problemas relacionados con parámetros, propiedades y conceptos de solución considerados para los juegos simples. Además consideramos casos extremos con respecto a la demanda de influencia, y probamos que para ciertas subfamilias, varios de estos problemas se vuelven polinomiales. Finalmente estudiamos algunas aplicaciones inspiradas en los juegos de influencia. El primer conjunto de estos resultados tiene que ver con la definición de modelos de decisión colectiva. Para sistemas de mediación, varios de los problemas de propiedades mencionados anteriormente son polinomialmente resolubles. Para los sistemas de influencia, demostramos que computar la satisfacción (una medida equivalente al índice de Rae y similar al valor de Banzhaf) es difícil a menos que consideremos algunas restricciones en el modelo. Para los sistemas OLFM, una generalización de los sistemas OLF (van den Brink et al. 2011, 2012) proporcionamos una axiomatización para la medida de satisfacción. El segundo conjunto de resultados se refiere al análisis de redes sociales, y en particular con la definición de nuevas medidas de centralidad de redes sociales, que comparamos en redes reales con otras medidas de centralidad clásicas

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Takhtamysheva, Aneta Verfasser], Rainer [Akademischer Betreuer] Malaka, and Andreas [Akademischer Betreuer] [Breiter. "Human Computation and Human Subject Tasks in Social Network Playful Applications / Aneta Takhtamysheva. Betreuer: Rainer Malaka. Gutachter: Rainer Malaka ; Andreas Breiter." Bremen : Staats- und Universitätsbibliothek Bremen, 2016. http://d-nb.info/1094955884/34.

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Graversen, Therese. "Statistical and computational methodology for the analysis of forensic DNA mixtures with artefacts." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:4c3bfc88-25e7-4c5b-968f-10a35f5b82b0.

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This thesis proposes and discusses a statistical model for interpreting forensic DNA mixtures. We develop methods for estimation of model parameters and assessing the uncertainty of the estimated quantities. Further, we discuss how to interpret the mixture in terms of predicting the set of contributors. We emphasise the importance of challenging any interpretation of a particular mixture, and for this purpose we develop a set of diagnostic tools that can be used in assessing the adequacy of the model to the data at hand as well as in a systematic validation of the model on experimental data. An important feature of this work is that all methodology is developed entirely within the framework of the adopted model, ensuring a transparent and consistent analysis. To overcome the challenge that lies in handling the large state space for DNA profiles, we propose a representation of a genotype that exhibits a Markov structure. Further, we develop methods for efficient and exact computation in a Bayesian network. An implementation of the model and methodology is available through the R package DNAmixtures.

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Sariyuce, Ahmet Erdem. "Fast Algorithms for Large-Scale Network Analytics." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1429825578.

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Lampert, Marco Andrei. "Análise Da Relevância De Mensagens No Twitter Através De Um Sistema Multi-Agente." Universidade do Vale do Rio dos Sinos, 2012. http://www.repositorio.jesuita.org.br/handle/UNISINOS/4547.

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Submitted by William Justo Figueiro (williamjf) on 2015-07-17T23:13:03Z No. of bitstreams: 1 35c.pdf: 1699465 bytes, checksum: d02851ad31e9068e1eae089ab0df0940 (MD5) Made available in DSpace on 2015-07-17T23:13:03Z (GMT). No. of bitstreams: 1 35c.pdf: 1699465 bytes, checksum: d02851ad31e9068e1eae089ab0df0940 (MD5) Previous issue date: 2012 Nenhuma O surgimento de novas tecnologias e as inovações em mídias sociais têm alterado a forma como as pessoas se comportam. Destacadamente as Redes Sociais estão cada vez mais inseridas na vida das pessoas. Nunca houve tanto desenvolvimento, penetração, diversificação, dispersão da informação, comunicação em tempo real, compressão do espaço e tempo, concomitante com a pluralidade de perspectivas, definições, análises e de cenários prospectivos sobre os possí- veis desdobramentos dos fatos do presente. Convergência está em todo lugar e nunca foi tão fácil atingir um público tão grande. Diante deste cenário exploramos as pesquisas existentes e propomos uma abordagem para analisar a relevância de mensagens do Twitter, monitorando a sua evolução na rede e estabelecendo a influência exercida em um espaço demográfico. Desenvolvemos uma aplicação capaz de fazer esta monitoração, com intuito de verificar e validar o modelo proposto. The appearance of new technologies and innovations in social media has changed the way how people behave themself. Remarkably Social Networks are more and more incorporated in people’s lives. There has never been so much development, penetration, diversification, dispersion, real-time communication, compression of space and time, concomitant with a plurality of perspectives, definitions, analysis and prospective scenarios on the possible unfolding of the facts of the present. Convergence is everywhere and has never been so easy to achieve such a large audience. In this scenario we explore the existing research and we propose an approach to analyze the relevance of Twitter messages, monitoring its evolution in the network and establishing the influence in a demographic space. We develop an application able to do this monitoring, with the intent to verify and validate the proposed model.

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CAMPEDELLI, GIAN MARIA. "ON META-NETWORKS, DEEP LEARNING, TIME AND JIHADISM." Doctoral thesis, Università Cattolica del Sacro Cuore, 2020. http://hdl.handle.net/10280/70552.

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Il terrorismo di stampo jihadista rappresenta una minaccia per la società e una sfida per gli scienziati interessati a comprenderne la complessità. Questa complessità richiede costantemente nuovi sviluppi in termini di ricerca sul terrorismo. Migliorare la conoscenza empirica rispetto a tale fenomeno può potenzialmente contribuire a sviluppare applicazioni concrete e, in ultima istanza, a prevenire danni all’uomo. In considerazione di tali aspetti, questa tesi presenta un nuovo quadro metodologico che integra scienza delle reti, modelli stocastici e apprendimento profondo per far luce sul terrorismo jihadista sia a livello esplicativo che predittivo. In particolare, questo lavoro compara e analizza le organizzazioni jihadiste più attive a livello mondiale (ovvero lo Stato Islamico, i Talebani, Al Qaeda, Boko Haram e Al Shabaab) per studiarne i pattern comportamentali e predirne le future azioni. Attraverso un impianto teorico che si poggia sulla concentrazione spaziale del crimine e sulle prospettive strategiche del comportamento terroristico, questa tesi persegue tre obiettivi collegati utilizzando altrettante tecniche ibride. In primo luogo, verrà esplorata la complessità operativa delle organizzazioni jihadiste attraverso l’analisi di matrici stocastiche di transizione e verrà presentato un nuovo coefficiente, denominato “Normalized Transition Similarity”, che misura la somiglianza fra paia di gruppi in termini di dinamiche operative. In secondo luogo, i processi stocastici di Hawkes aiuteranno a testare la presenza di meccanismi di dipendenza temporale all’interno delle più comuni sotto-sequenze strategiche di ciascun gruppo. Infine, il framework integrerà la meta-reti complesse e l’apprendimento profondo per classificare e prevedere i target a maggiore rischio di essere colpiti dalle organizzazioni jihadiste durante i loro futuri attacchi. Per quanto riguarda i risultati, le matrici stocastiche di transizione mostrano che i gruppi terroristici possiedono un ricco e complesso repertorio di combinazioni in termini di armi e obiettivi. Inoltre, i processi di Hawkes indicano la presenza di diffusa self-excitability nelle sequenze di eventi. Infine, i modelli predittivi che sfruttano la flessibilità delle serie temporali derivanti da grafi dinamici e le reti neurali Long Short-Term Memory forniscono risultati promettenti rispetto ai target più a rischio. Nel complesso, questo lavoro ambisce a dimostrare come connessioni astratte e nascoste fra eventi possano essere fondamentali nel rivelare le meccaniche del comportamento jihadista e come processi memory-like (ovvero molteplici comportamenti ricorrenti, interconnessi e non randomici) possano risultare estremamente utili nel comprendere le modalità attraverso cui tali organizzazioni operano. Jihadist terrorism represents a global threat for societies and a challenge for scientists interested in understanding its complexity. This complexity continuously calls for developments in terrorism research. Enhancing the empirical knowledge on the phenomenon can potentially contribute to developing concrete real-world applications and, ultimately, to the prevention of societal damages. In light of these aspects, this work presents a novel methodological framework that integrates network science, mathematical modeling, and deep learning to shed light on jihadism, both at the explanatory and predictive levels. Specifically, this dissertation will compare and analyze the world's most active jihadist terrorist organizations (i.e. The Islamic State, the Taliban, Al Qaeda, Boko Haram, and Al Shabaab) to investigate their behavioral patterns and forecast their future actions. Building upon a theoretical framework that relies on the spatial concentration of terrorist violence and the strategic perspective of terrorist behavior, this dissertation will pursue three linked tasks, employing as many hybrid techniques. Firstly, explore the operational complexity of jihadist organizations using stochastic transition matrices and present Normalized Transition Similarity, a novel coefficient of pairwise similarity in terms of strategic behavior. Secondly, investigate the presence of time-dependent dynamics in attack sequences using Hawkes point processes. Thirdly, integrate complex meta-networks and deep learning to rank and forecast most probable future targets attacked by the jihadist groups. Concerning the results, stochastic transition matrices show that terrorist groups possess a complex repertoire of combinations in the use of weapons and targets. Furthermore, Hawkes models indicate the diffused presence of self-excitability in attack sequences. Finally, forecasting models that exploit the flexibility of graph-derived time series and Long Short-Term Memory networks provide promising results in terms of correct predictions of most likely terrorist targets. Overall, this research seeks to reveal how hidden abstract connections between events can be exploited to unveil jihadist mechanics and how memory-like processes (i.e. multiple non-random parallel and interconnected recurrent behaviors) might illuminate the way in which these groups act.

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Theodoni, Panagiota. "Fluctuations in perceptual decisions : cortical microcircuit dynamics mediating alternations in conscious visual perception." Doctoral thesis, Universitat Pompeu Fabra, 2014. http://hdl.handle.net/10803/145642.

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Fluctuations in perceptual decisions emerge when our brain confronts with ambiguous sensory stimuli. For instance, our perception alternates between two conflicting images when presented dichoptically to our eyes, allowing a dissociation of the sensory stimulation from the conscious visual perception, and therefore providing a gateway to consciousness. How does the brain work when it deals with such ambiguous sensory stimuli? We addressed this question theoretically by employing a biophysically realistic attractor network, by consistently reducing it to a four- variable rate- based model, and by extracting analytical expressions for second- order statistics. We considered human behavioral and macaque neurophysiological data collected when subjects were confronting with such ambiguities. Our results show the relevance of neuronal adaptation in perceptual decision making, as well as that it contributes to the speed- accuracy trade- off. Furthermore, our findings affirm that both noise and neural adaptation operate in balance during the fluctuating states of visual awareness and suggest that while adaptation in inhibition is not relevant for the perceptual alternations, it contributes to the brain dynamics at rest. Finally, we explain the observed neuronal noise- decorrelation during visual consciousness and provide insights on the long- standing question: where in the brain rivalry is resolved. Les fluctuacions en les decisions perceptives sorgeixen quan el nostre cervell s'enfronta a estímuls sensorials ambigus. Per exemple, la nostra percepció alterna entre dues imatges contradictòries quan es presenten de forma dicòptica als nostres ulls, cosa que permet una dissociació de l'estimulació sensorial de la percepció visual conscient, i per tant proporciona una porta d'entrada a la consciència. Com funciona el cervell quan es tracta d'aquest tipus d'estímuls sensorials ambigus? Hem tractat aquesta qüestió de forma teòrica mitjançant l'ús d'una xarxa d'atractors biofísicament realista, reduint-la de forma consistent a un model de quatre variables basat en la freqüència, i extraient expressions analítiques pels estadístics de segon ordre. Hem emprat dades neurofisiològiques de comportament d'humans i macacos recollides quan els subjectes s'enfrontaven a aquest tipus d'ambigüitats. Els nostres resultats mostren la importància de l'adaptació neuronal en la presa de decisions perceptives i mostren la seva contribució a l'equilibri velocitat-precisió. D'altra banda, els nostres resultats confirmen que tant el soroll com l'adaptació neural operen en equilibri durant els estats fluctuants de consciència visual i suggereixen que, si bé l'adaptació en la inhibició no és rellevant per a les alternances de percepció, contribueix a la dinàmica del cervell en repòs. Finalment, expliquem la decorrelació del soroll neuronal observada durant la consciència visual i proporcionem noves idees en relació a l’antiga qüestió de en quin lloc del cervell es resol la rivalitat visual.

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Li, Vigni Guido Fabrizio. "Les systèmes complexes et la digitalisation des sciences. Histoire et sociologie des instituts de la complexité aux États-Unis et en France." Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEH134/document.

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Comment penser la relation entre les cultures scientifiques contemporaines et l’usage grandissant de l’ordinateur dans la production des savoirs ? Cette thèse se propose de donner une réponse à telle question à partir de l’analyse historique et sociologique d’un domaine scientifique fondé par le Santa Fe Institute (SFI) dans les années 1980 aux États-Unis : les « sciences des systèmes complexes » (SSC). Rendues célèbres par des publications grand-public, les SSC se répandent au cours des années 1990 et 2000 en Europe et dans d’autres pays du monde. Ce travail propose une histoire de la fondation de ce domaine en se concentrant sur le SFI et sur le Réseau National des Systèmes Complexes français. Avec un regard sociologique ancré dans les Science & Technology Studies et dans le courant pragmatiste, elle pose ensuite des questions sur le statut socio-épistémique de ce domaine, sur les modalités de l’administration de la preuve dans des savoirs fondés sur la simulation numérique et enfin sur les engagements épistémiques tenus par les spécialistes des systèmes complexes. Le matériau empirique – composé d’environ 200 entretiens, plusieurs milliers de pages d’archives et quelques visites de laboratoire – nous amène non seulement à mieux connaître ce champ de recherche – dont le langage est très répandu aujourd’hui, mais peu étudié par les historiens et les sociologues ; il nous porte aussi à questionner trois opinions courantes dans la littérature humaniste à propos des sciences numériques. À savoir : 1) l’ordinateur produit des connaissances de plus en plus interdisciplinaires, 2) il donne vie à des savoirs de type nouveau qui nécessitent une toute autre épistémologie pour être pensés et 3) il fait inévitablement advenir des visions du monde néolibérales. Or, cette thèse déconstruit ces trois formes de déterminisme technologique concernant les effets de l’ordinateur sur les pratiques scientifiques, en montrant d’abord que, dans les sciences computationnelles, les rapports interdisciplinaires ne se font pas sans effort ni pacifiquement ou sur pied d’égalité ; ensuite que les chercheurs et les chercheuses des SSC mobilisent des formes d’administration de la preuve déjà mises au point dans d’autres disciplines ; et enfin que les engagements épistémiques des scientifiques peuvent prendre une forme proche de la vision (néo)libérale, mais aussi des formes qui s’en éloignent ou qui s’y opposent How to think the relationship between contemporary scientific cultures and the rising usage of computer in the production of knowledge ? This thesis offers to give an answer to such a question, by analyzing historically and sociologically a scientific domain founded by the Santa Fe Institute (SFI) in the 1980s in the United States : the « complex systems sciences » (CSS). Become well-known thanks to popular books and articles, CSS have spread in Europe and in other countries of the world in the course of the 1990s and the 2000s. This work proposes a history of the foundation of this domain, by focussing on the SFI and on the French Complex Systems National Network. With a sociological take rooted into Science & Technology Studies and into pragmatism, it then asks some questions about the socio-epistemic status of such a domain, about the modalities of production of evidence as they are employed in the context of digital simulation and, finally, about the epistemic engagements hold by complexity specialists. Empirical material – composed by circa 200 interviews, several thousands archival pages and a small number of laboratory visits – allows us not only to improve knowledge about this field – whose language is very common today, but little studied by historians and sociologists ; it also brings us to question three current opinions in the human and social sciences literature regarding digital sciences. That is : 1) that the computer produces more and more interdisciplinary knowledge, 2) that it gives birth to a new type of knowledge which needs an entirely new epistemology to be well understood and 3) that it inevitably brings about neoliberal visions of the world. Now, this thesis deconstructs these three forms of technological determinism concerning the effects of computer on scientific practices, by showing firstly that, in digital sciences, the interdisciplinary collaborations are not made without any effort and in a symetrical and pacific way ; secondly, that CSS’ researchers mobilize a kind of evidence production techniques which are well known in other disciplines ; and, thirdly, that scientists’ epistemic engagements can take (neo)liberal forms, but also other forms that depart from neoliberalism or that stand against it

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Navarro, Emmanuel. "Métrologie des graphes de terrain, application à la construction de ressources lexicales et à la recherche d'information." Phd thesis, Institut National Polytechnique de Toulouse - INPT, 2013. http://tel.archives-ouvertes.fr/tel-01020232.

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Cette thèse s'organise en deux parties : une première partie s'intéresse aux mesures de similarité (ou de proximité) définies entre les sommets d'un graphe, une seconde aux méthodes de clustering de graphe biparti. Une nouvelle mesure de similarité entre sommets basée sur des marches aléatoires en temps courts est introduite. Cette méthode a l'avantage, en particulier, d'être insensible à la densité du graphe. Il est ensuite proposé un large état de l'art des similarités entre sommets, ainsi qu'une comparaison expérimentale de ces différentes mesures. Cette première partie se poursuit par la proposition d'une méthode robuste de comparaison de graphes partageant le même ensemble de sommets. Cette méthode est mise en application pour comparer et fusionner des graphes de synonymie. Enfin une application d'aide à la construction de ressources lexicales est présentée. Elle consiste à proposer de nouvelles relations de synonymie à partir de l'ensemble des relations de synonymie déjà existantes. Dans une seconde partie, un parallèle entre l'analyse formelle de concepts et le clustering de graphe biparti est établi. Ce parallèle conduit à l'étude d'un cas particulier pour lequel une partition d'un des groupes de sommets d'un graphe biparti peut-être déterminée alors qu'il n'existe pas de partitionnement correspondant sur l'autre type de sommets. Une méthode simple qui répond à ce problème est proposée et évaluée. Enfin Kodex, un système de classification automatique des résultats d'une recherche d'information est présenté. Ce système est une application en RI des méthodes de clustering vues précédemment. Une évaluation sur une collection de deux millions de pages web montre les avantages de l'approche et permet en outre de mieux comprendre certaines différences entre méthodes de clustering.

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Tsai, Yu-Shiuan, and 蔡宇軒. "Network-based Computational Epidemiology: A Multilayer Framework Integrating Social Networks with Epidemic Dynamics." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/60758616028093627810.

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博士 國立交通大學 資訊科學與工程研究所 99 Network-based computational epidemiologists use computers and either theoretical or actual network topologies to study the transmission dynamics of human diseases and social trends. In this dissertation I discuss the importance, current status, advantages, and modeling procedures of network-based computational epidemiology, specifically presenting three original studies in detail. The first study is an investigation of how resources and transmission costs influence diffusion dynamics and tipping points in scale-free networks. An epidemic model based on an analytic equation is proposed to explain the existence of epidemic critical thresholds in scale-free networks. Study results suggest the possibility of controlling the spread of epidemics in scale-free networks by manipulating resources and costs associated with an infection event. In the second study, a proposal for a multilayer epidemiological framework that integrates realistic social networks, called the Multilayer Epidemic Dynamics Simulator (MEDSim), is described from individual and national perspectives. Model flexibility and generalizability are tested using outbreak locations and intervention scenarios for the 2009 A/H1N1 influenza epidemic in Taiwan. The results coincide with the dynamic processes of epidemics under different intervention scenarios, thus clarifying the effects of complex contact structures on disease transmission dynamics. In the third study, the potential benefits of epidemic simulations and instructions for building network-based epidemic models by novices learning network-based computational epidemiology approaches is investigated. The goal is to help individuals with less advanced computing skills build epidemiological models, determine appropriate simulation parameters, and construct operational procedures. It is my hope that the studies presented in this dissertation can assist in efforts by public health organizations to correctly implement intervention strategies by using simulations to analyze multilayer interactions.

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"Algorithms and computational complexity of social influence and diffusion problems in social networks." 2015. http://repository.lib.cuhk.edu.hk/en/item/cuhk-1291255.

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Since diffusion models of social network are widely used in studying epidemiology, in this thesis, we apply diffusion models to study the contact immunity generated by attenuated vaccines.Oral polio vaccine (OPV) is a typical attenuated vaccine for polio that can produce contact immunity and therefore help protect more individuals than vaccinees. To better capture the utilization of OPV’s contact immunity, we model the community as a social network, and formulate the task of maximizing the contact immunity effect as an optimization problem on graphs, which is to find a sequence of vertices to be “vaccinated” to maximize the total number of vertices “infected” by the attenuated virus. Furthermore, as immune defiicient patients may suffer from the live attenuated virus in the vaccine, we develop models in consideration of this restriction, and study related problems. We present polynomial-time algorithms for these problems on trees, and show the intractability of problems on general graphs. 社交網絡的擴散模型被廣泛運用于對流行病學的研究，在本文中，我們使用擴散模型對減毒活疫苗產生的接觸性免疫進行研究。口服脊髓灰質炎疫苗（OPV）是一種典型的減毒活疫苗，它可以在人群中產生接觸性免疫，使得更多未接種疫苗的人獲得免疫力。 爲了更好的刻畫OPV 產生的接觸性免疫，我們將社區模型化為社交網絡，從而將接觸性免疫效應最大化的任務轉化爲圖優化問題，即通過發現頂點的一個「接種」序列來最大化被減活病毒「感染」的頂點數量。此外，因爲減毒疫苗中的活病毒會使患有免疫缺陷的病人患病，我們考慮在此因素限制下的模型，并研究相關的問題。 我們給出這些問題在樹上的多項式時間算法，并證明其在一般圖上的複雜性。 Ma, Chenglong. Thesis M.Phil. Chinese University of Hong Kong 2015. Includes bibliographical references (leaves 40-47). Abstracts also in Chinese. Title from PDF title page (viewed on 12, September, 2016). Detailed summary in vernacular field only. Detailed summary in vernacular field only. Detailed summary in vernacular field only.

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Gupta, Mona. "Anaytics on behavior of users on large datasets." Thesis, 2017. http://localhost:8080/iit/handle/2074/7294.

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Waniek, Marcin. "Hiding in Social Networks." Doctoral thesis, 2017. https://depotuw.ceon.pl/handle/item/2174.

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The Internet and social media have fuelled enormous interest in social network analysis. New tools continue to be developed and used to analyse our personal connections. This raises privacy concerns that are likely to exacerbate in the future. With this in mind, we ask the question: Can individuals or groups actively manage their connections to evade social network analysis tools? By addressing this question, the general public may better protect their privacy, oppressed activist groups may better conceal their existence, and security agencies may better understand how terrorists escape detection.In this dissertation we consider hiding from three different types of social network analysis tools. First, we study how both an individual and a group of nodes can evade analysis utilizing centrality measures, without compromising ability to participate in network's activities. In the second study, we investigate how a community can avoid being identified by a community detection algorithm as a closely cooperating group of nodes. In the third study, we analyse how a presence of a particular edge in a network can be hidden from link prediction algorithms.For considered problems we analyse their computational complexity and prove that they are usually NP-hard.However, we also provide polynomial-time heuristic solutions that turn out to be effective in practice. We test our algorithms on a number of real-life and artificially generated network datasets. Internet oraz media społecznościowe spowodowały ogromny wzrost zainteresowania metodami analizy sieci społecznych. Coraz bardziej zaawansowane narzędzia służą do analizy naszych powiązań z innymi ludźmi.Rodzi to poważne obawy związane z prywatnością. Mając to na uwadze, rozważamy następujące pytanie: Czy członek lub grupa członków sieci społecznej może aktywnie zarządzać swoimi połączeniami tak, aby uniknąć wykrycia przez narzędzia analizy sieci społecznych? Odpowiedź na to pytanie pozwoliłaby użytkownikom Internetu lepiej chronić swoją prywatność, grupom aktywistów lepiej ukrywać swoją działalność, a agencjom bezpieczeństwa lepiej rozumieć w jaki sposób organizacje terrorystyczne i kryminalne mogą unikać wykrycia.W tej pracy rozważamy ukrywanie się przed trzema różnymi narzędziami analizy sieci społecznych. Po pierwsze, badamy jak pojedynczy węzeł lub ich grupa może uniknąć wykrycia przez miary centralności (ang. centrality measures), wciąż pozostając zdolnym do brania udziału w działalności sieci. Po drugie, analizujemy jak grupa węzłów może uniknąć zidentyfikowania przez algorytmy wykrywania społeczności (ang. community detection algorithms). Po trzecie wreszcie, badamy jak można ukryć istnienie określonej krawędzi w sieci przed algorytmami przewidywania połączeń (ang. link prediction algorithms).Analizujemy złożoność obliczeniową rozważanych zagadnień oraz udowadniamy, że większość z nich to problemy NP-trudne. Tym niemniej prezentujemy również wielomianowe rozwiązania heurystyczne, które okazują się efektywne w praktyce. Nasze algorytmy testujemy na szeregu różnych sieci, tak prawdziwych, jak i wygenerowanych losowo.

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Zhang, Shaodian. "Computational Approaches to Characterizing Online Health Communities." Thesis, 2016. https://doi.org/10.7916/D82Z15JX.

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Online health communities (OHCs) have been increasingly popular among patients with chronic or life-threatening illnesses for the exchange of social support. Contemporary research of OHCs relies on methods and tools to handle analytics of massive user-generated content at scale to complement traditional qualitative analysis. In this thesis, we aim at advancing the area of research by providing computational tools and methods which facilitate automated content analysis, and by presenting applications of these tools to investigating member characteristics and behaviors. We first provide a framework of conceptualization to systematically describe problems, challenges, and existing solutions for OHCs from a social support standpoint, to bridge the knowledge gap between health psychology and informatics. With this framework in hand, we define the landscape of online social support, summarize current research progress of OHCs, and identify research questions to investigate for this thesis. We then build a series of computational tools for analyzing OHC content, relying on techniques of machine learning and natural language processing. Leveraging domain-specific features, our tools are tailored to handle content analysis tasks on OHC text effectively. Equipped with computational tools, we demonstrate how characteristics of OHC members can be identified at scale in an automated fashion. In particular, we build up multi-dimensional descriptions for patient members, consisting of what topics they focus on, what sentiment they express, and what treatments they discuss and adopt. Patterns of how these member characteristics change through time are also investigated longitudinally. Finally, relying on computational analytics, members' behaviors of engagement such as debate and dropping-out are identified and characterized. Studies presented in this thesis discover static and longitudinal patterns of member characteristics and engagement, which are potential research hypotheses to be explored by health psychologists and clinical researchers. The thesis also contributes to the informatics community by making computational tools, lexicons, and annotated corpora available to facilitate future research.

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Gruzd, Anatoliy A., and Caroline Haythornthwaite. "Automated Discovery and Analysis of Social Networks from Threaded Discussions." 2008. http://hdl.handle.net/10150/105081.

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To gain greater insight into the operation of online social networks, we applied Natural Language Processing (NLP) techniques to text-based communication to identify and describe underlying social structures in online communities. This paper presents our approach and preliminary evaluation for content-based, automated discovery of social networks. Our research question is: What syntactic and semantic features of postings in a threaded discussions help uncover explicit and implicit ties between network members, and which provide a reliable estimate of the strengths of interpersonal ties among the network members? To evaluate our automated procedures, we compare the results from the NLP processes with social networks built from basic who-to-whom data, and a sample of hand-coded data derived from a close reading of the text. For our test case, and as part of ongoing research on networked learning, we used the archive of threaded discussions collected over eight iterations of an online graduate class. We first associate personal names and nicknames mentioned in the postings with class participants. Next we analyze the context in which each name occurs in the postings to determine whether or not there is an interpersonal tie between a sender of the posting and a person mentioned in it. Because information exchange is a key factor in the operation and success of a learning community, we estimate and assign weights to the ties by measuring the amount of information exchanged between each pair of the nodes; information in this case is operationalized as counts of important concept terms in the postings as derived through the NLP analyses. Finally, we compare the resulting network(s) against those derived from other means, including basic who-to-whom data derived from posting sequences (e.g., whose postings follow whose). In this comparison we evaluate what is gained in understanding network processes by our more elaborate analyses.

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Gruzd, Anatoliy. "Name Networks: A Content-Based Method for Automated Discovery of Social Networks to Study Collaborative Learning." 2009. http://hdl.handle.net/10150/105553.

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As a way to gain greater insight into the operation of Library and Information Science (LIS) e-learning communities, the presented work applies automated text mining techniques to text-based communication to identify, describe and evaluate underlying social networks within such communities. The main thrust of the study is to find a way to use computers to automatically discover social ties that form between students just from their threaded discussions. Currently, one of the most common but time consuming methods for discovering social ties between people is to ask questions about their perceived social ties via a survey. However, such a survey is difficult to collect due to the high cost associated with data collection and the sensitive nature of the types of questions that must be asked. To overcome these limitations, the paper presents a new, content-based method for automated discovery of social networks from threaded discussions dubbed name networks. When fully developed, name networks can be used as a real time diagnostic tool for educators to evaluate and improve teaching models and to identify students who might need additional help or students who may provide such help to others.

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"Three Facets of Online Political Networks: Communities, Antagonisms, and Polarization." Doctoral diss., 2019. http://hdl.handle.net/2286/R.I.55512.

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abstract: Millions of users leave digital traces of their political engagements on social media platforms every day. Users form networks of interactions, produce textual content, like and share each others' content. This creates an invaluable opportunity to better understand the political engagements of internet users. In this proposal, I present three algorithmic solutions to three facets of online political networks; namely, detection of communities, antagonisms and the impact of certain types of accounts on political polarization. First, I develop a multi-view community detection algorithm to find politically pure communities. I find that word usage among other content types (i.e. hashtags, URLs) complement user interactions the best in accurately detecting communities. Second, I focus on detecting negative linkages between politically motivated social media users. Major social media platforms do not facilitate their users with built-in negative interaction options. However, many political network analysis tasks rely on not only positive but also negative linkages. Here, I present the SocLSFact framework to detect negative linkages among social media users. It utilizes three pieces of information; sentiment cues of textual interactions, positive interactions, and socially balanced triads. I evaluate the contribution of each three aspects in negative link detection performance on multiple tasks. Third, I propose an experimental setup that quantifies the polarization impact of automated accounts on Twitter retweet networks. I focus on a dataset of tragic Parkland shooting event and its aftermath. I show that when automated accounts are removed from the retweet network the network polarization decrease significantly, while a same number of accounts to the automated accounts are removed randomly the difference is not significant. I also find that prominent predictors of engagement of automatically generated content is not very different than what previous studies point out in general engaging content on social media. Last but not least, I identify accounts which self-disclose their automated nature in their profile by using expressions such as bot, chat-bot, or robot. I find that human engagement to self-disclosing accounts compared to non-disclosing automated accounts is much smaller. This observational finding can motivate further efforts into automated account detection research to prevent their unintended impact. Dissertation/Thesis Doctoral Dissertation Computer Science 2019

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Branzei, Simina. "Two Coalitional Models for Network Formation and Matching Games." Thesis, 2011. http://hdl.handle.net/10012/6112.

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This thesis comprises of two separate game theoretic models that fall under the general umbrella of network formation games. The first is a coalitional model of interaction in social networks that is based on the idea of social distance, in which players seek interactions with similar others. Our model captures some of the phenomena observed on such networks, such as homophily driven interactions and the formation of small worlds for groups of players. Using social distance games, we analyze the interactions between players on the network, study the properties of efficient and stable networks, relate them to the underlying graphical structure of the game, and give an approximation algorithm for finding optimal social welfare. We then show that efficient networks are not necessarily stable, and stable networks do not necessarily maximise welfare. We use the stability gap to investigate the welfare of stable coalition structures, and propose two new solution concepts with improved welfare guarantees. The second model is a compact formulation of matchings with externalities. Our formulation achieves tractability of the representation at the expense of full expressivity. We formulate a template of solution concept that applies to games where externalities are involved, and instantiate it in the context of optimistic, neutral, and pessimistic reasoning. Then we investigate the complexity of the representation in the context of many-to-many and one-to-one matchings, and provide both computational hardness results and polynomial time algorithms where applicable.

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Shabut, Antesar R. M., Keshav P. Dahal, and Irfan U. Awan. "Friendship based trust model to secure routing protocols in mobile Ad Hoc networks." 2014. http://hdl.handle.net/10454/10787.

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No Trust management in mobile ad hoc networks (MANETs) has become a significant issue in securing routing protocols to choose reliable and trusted paths. Trust is used to cope with defection problems of nodes and stimulate them to cooperate. However, trust is a highly complex concept because of the subjective nature of trustworthiness, and has several social properties, due to its social origins. In this paper, a friendship-based trust model is proposed for MANETs to secure routing protocol from source to destination, in which multiple social degrees of friendships are introduced to represent the degree of nodes' trustworthiness. The model considers the behaviour of nodes as a human pattern to reflect the complexity of trust subjectivity and different views. More importantly, the model considers the dynamic differentiation of friendship degree over time, and utilises both direct and indirect friendship-based trust information. The model overcomes the limitation of neglecting the social behaviours of nodes when evaluating trustworthiness. The empirical analysis shows the greater robustness and accuracy of the trust model in a dynamic MANET environment.

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Spiliotopoulos, Anastasios. "Studying social network sites with the combination of traditional social science and computational approaches." Doctoral thesis, 2020. http://hdl.handle.net/10400.13/3130.

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Social Network Sites (SNSs) are fundamentally changing the way humans connect, communicate and relate to one another and have attracted a considerable amount of research attention. In general, two distinct research approaches have been followed in the pursuit of results in this research area. First, established traditional social science methods, such as surveys and interviews, have been extensively used for inquiry-based research on SNSs. More recently, however, the advent of Application Programming Interfaces (APIs) has enabled data-centric approaches that have culminated in theory-free “big data” studies. Both of these approaches have advantages, disadvantages and limitations that need to be considered in SNS studies. The objective of this dissertation is to demonstrate how a suitable combination of these two approaches can lead to a better understanding of user behavior on SNSs and can enhance the design of such systems. To this end, I present two two-part studies that act as four pieces of evidence in support of this objective. In particular, these studies investigate whether a combination of survey and API-collected data can provide additional value and insights when a) predicting Facebook motivations, b) understanding social media selection, c) understanding patterns of communication on Facebook, and d) predicting and modeling tie strength, compared to what can be gained by following a traditional social science or a computational approach in isolation. I then discuss how the findings from these studies contribute to our understanding of online behavior both at the individual user level, e.g. how people navigate the SNS ecosystem, and at the level of dyadic relationships, e.g. how tie strength and interpersonal trust affect patterns of dyadic communication. Furthermore, I describe specific implications for SNS designers and researchers that arise from this work. For example, the work presented has theoretical implications for the Uses and Gratifications (U&G) framework and for the application of Rational Choice Theory (RCT) in the context of SNS interactions, and design implications such as enhancing SNS users’ privacy and convenience by supporting reciprocity of interactions. I also explain how the results of the conducted studies demonstrate the added value of combining traditional social science and computational methods for the study of SNSs, and, finally, I provide reflections on the strengths and limitations of the overall research approach that can be of use to similar research efforts. As Redes Sociais (SNSs - Social Network Sites) estão a mudar de form fundamental a maneira como os seres humanos estabelecem ligações entre si, como comunicam e como relacionam-se uns com os outros, tendo atraído uma considerável quantidade de atenção investigativa. Em geral, duas abordagens de investigação distintas foram seguidas na procura de resultados nesta área de investigação. Em primeiro lugar, os já estabelecidos métodos tradicionais das ciências sociais, tais como inquéritos e entrevistas foram amplamente utilizados na investigação baseada em SNSs. Contudo, o surgimento mais recente das Interfaces de Programação de Aplicações (APIs - Application Programming Interfaces) tem permitido abordagens centradas em dados que têm culminado em estudos de "dados extensos", livres de teoria. Ambas estas abordagens têm vantagens, desvantagens e limitações que precisam de ser consideradas nos estudos de SNS. O objectivo desta dissertação é demonstrar como uma combinação adequada destas duas abordagens pode levar a uma melhor compreensão do comportamento do utilizador em SNSs e pode melhorar a concepção de tais sistemas. Para esse efeito, apresento dois estudos, em duas partes, que funcionam como quatro peças de prova em apoio a este objectivo. Estes estudos investigam, em particular, se uma combinação de dados recolhidos através de inquéritos e API pode fornecer valor adicional e conhecimentos ao a) prever as motivações do Facebook, b) compreender a selecção dos meios de comunicação social, c) compreender os padrões de comunicação no Facebook, e d) prever e modelar a força dos laços, em comparação com o que pode ser ganho seguindo uma ciência social tradicional ou uma abordagem computacional isolada. Abordo em seguida como os resultados destes estudos contribuem para uma compreensão do comportamento online tanto a nível do utilizador individual, por exemplo, como as pessoas percorrem o ecossistema SNS, e ao nível das relações diádicas, por exemplo, como a força dos laços e a confiança interpessoal afectam os padrões de comunicação diádica. Além disso, descrevo as implicações específicas para os designers e investigadores do SNS que decorrem deste trabalho. Por exemplo, o trabalho apresentado tem implicações teóricas para o quadro de Usos e Gratificações (U&G - Uses and Gratifications framework) e para a aplicação da Teoria da Escolha Racional (RCT - Rational Choice Theory) no contexto das interacções SNS, e implicações de design, como o reforço da privacidade e conveniência dos utilizadores de SNS, com o apoio à reciprocidade das interacções. Explico também como os resultados dos estudos realizados demonstram o valor acrescentado de combinar as ciências sociais tradicionais e os métodos computacionais para o estudo de SNS, e, por fim, apresento reflexões sobre os pontos fortes e limitações da abordagem global de investigação que podem ser úteis a esforços de investigação semelhantes.

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LIAO, YU-FENG, and 廖裕楓. "Community-Oriented Credit Computation for Distributed Social Network Platform." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/89279827683678964370.

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碩士 國立中央大學 資訊工程學系 105 This thesis wants to design a community-oriented credit s on social network platform. The community-oriented credit is based on the personalized credit on WeOS platform. The personalized credit is based on actions and reputations on the internet. Reputations on the internet are global. All people see the same score. It does not have personalized difference. Personalized credit has personalized difference. But has heavy computation. Community-oriented credit is between both. This thesis wants to apply a community-oriented credit on WeOS platform.The WeOS platform is a web platform based on P2P (Peer-to-Peer) structure.On the platform, users can use many service such as forum provided by themself or other people. This thesis use the simulating data to compute community-oriented credit with distributed structure. The dataset is divided by different number of user to compute time. Finally, the thesis designs a community-oriented credit that can work on social network platform.

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