Relevant bibliographies by topics / Computational social networks

Academic literature on the topic 'Computational social networks'

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Published: 6 September 2023

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Journal articles on the topic "Computational social networks"

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Nasution, Mahyuddin K. M., Rahmad Syah, and Marischa Elveny. "Social Network Analysis: Towards Complexity Problem." Webology 18, no. 2 (December 23, 2021): 449–61. http://dx.doi.org/10.14704/web/v18i2/web18332.

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Social network analysis is a advances from field of social networks. The structuring of social actors, with data models and involving intelligence abstracted in mathematics, and without analysis it will not present the function of social networks. However, graph theory inherits process and computational procedures for social network analysis, and it proves that social network analysis is mathematical and computational dependent on the degree of nodes in the graph or the degree of social actors in social networks. Of course, the process of acquiring social networks bequeathed the same complexity toward the social network analysis, where the approach has used the social network extraction and formulated its consequences in computing.
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Penn, A. "Synthetic networks — Spatial, social, structural and computational." BT Technology Journal 24, no. 3 (July 2006): 49–56. http://dx.doi.org/10.1007/s10550-006-0075-0.

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Wu, Jia, Fangfang Gou, Wangping Xiong, and Xian Zhou. "A Reputation Value-Based Task-Sharing Strategy in Opportunistic Complex Social Networks." Complexity 2021 (November 26, 2021): 1–16. http://dx.doi.org/10.1155/2021/8554351.

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As the Internet of Things (IoT) smart mobile devices explode in complex opportunistic social networks, the amount of data in complex networks is increasing. Large amounts of data cause high latency, high energy consumption, and low-reliability issues when dealing with computationally intensive and latency-sensitive emerging mobile applications. Therefore, we propose a task-sharing strategy that comprehensively considers delay, energy consumption, and terminal reputation value (DERV) for this context. The model consists of a task-sharing decision model that integrates latency and energy consumption, and a reputation value-based model for the allocation of the computational resource game. The two submodels apply an improved particle swarm algorithm and a Lagrange multiplier, respectively. Mobile nodes in the complex social network are given the opportunity to make decisions so that they can choose to share computationally intensive, latency-sensitive computing tasks to base stations with greater computing power in the same network. At the same time, to prevent malicious competition from end nodes, the base station decides the allocation of computing resources based on a database of reputation values provided by a trusted authority. The simulation results show that the proposed strategy can meet the service requirements of low delay, low power consumption, and high reliability for emerging intelligent applications. It effectively realizes the overall optimized allocation of computation sharing resources and promotes the stable transmission of massive data in complex networks.
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Nwanga, E. M., K. C. Okafor, G. A. Chukwudebe, and I. E. Achumba. "Computational Robotics: An Alternative Approach for Predicting Terrorist Networks." International Journal of Robotics and Automation Technology 8 (November 24, 2021): 1–11. http://dx.doi.org/10.31875/2409-9694.2021.08.1.

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Increasing terrorist activities globally have attracted the attention of many researchers, policy makers and security agencies towards counterterrorism. The clandestine nature of terrorist networks have made them difficult for detection. Existing works have failed to explore computational characterization to design an efficient threat-mining surveillance system. In this paper, a computationally-aware surveillance robot that auto-generates threat information, and transmit same to the cloud-analytics engine is developed. The system offers hidden intelligence to security agencies without any form of interception by terrorist elements. A miniaturized surveillance robot with Hidden Markov Model (MSRHMM) for terrorist computational dissection is then derived. Also, the computational framework for MERHMM is discussed while showing the adjacency matrix of terrorist network as a determinant factor for its operation. The model indicates that the terrorist network have a property of symmetric adjacency matrix while the social network have both asymmetric and symmetric adjacency matrix. Similarly, the characteristic determinant of adjacency matrix as an important operator for terrorist network is computed to be -1 while that of a symmetric and an asymmetric in social network is 0 and 1 respectively. In conclusion, it was observed that the unique properties of terrorist networks such as symmetric and idempotent property conferred a special protection for the terrorist network resilience. Computational robotics is shown to have the capability of utilizing the hidden intelligence in attack prediction of terrorist elements. This concept is expected to contribute in national security challenges, defense and military intelligence.
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Atdag, Samet, and Haluk O. Bingol. "Computational models for commercial advertisements in social networks." Physica A: Statistical Mechanics and its Applications 572 (June 2021): 125916. http://dx.doi.org/10.1016/j.physa.2021.125916.

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Ismaili, Anisse, and Patrice Perny. "Computational social choice for coordination in agent networks." Annals of Mathematics and Artificial Intelligence 77, no. 3-4 (June 13, 2015): 335–59. http://dx.doi.org/10.1007/s10472-015-9462-x.

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Tomassini, Marco, and Alberto Antonioni. "Computational Behavioral Models for Public Goods Games on Social Networks." Games 10, no. 3 (September 2, 2019): 35. http://dx.doi.org/10.3390/g10030035.

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Cooperation is a fundamental aspect of well-organized societies and public good games are a useful metaphor for modeling cooperative behavior in the presence of strong incentives to free ride. Usually, social agents interact to play a public good game through network structures. Here, we use social network structures and computational agent rules inspired by recent experimental work in order to develop models of agent behavior playing public goods games. The results of our numerical simulations based on a couple of simple models show that agents behave in a manner qualitatively similar to what has been observed experimentally. Computational models such as those presented here are very useful to interpret observed behavior and to enhance computationally the limited variation that is possible in the experimental domain. By assuming a priori reasonable individual behaviors, the easiness of running simulations could also facilitate exploration prior to any experimental work in order to vary and estimate a number of key parameters that would be very difficult, if not impossible, to change during the actual experiment.
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Li, Wei, and Sisi Zlatanova. "Significant Geo-Social Group Discovery over Location-Based Social Network." Sensors 21, no. 13 (July 2, 2021): 4551. http://dx.doi.org/10.3390/s21134551.

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Geo-social community detection over location-based social networks combining both location and social factors to generate useful computational results has attracted increasing interest from both industrial and academic communities. In this paper, we formulate a novel community model, termed geo-social group (GSG), to enforce both spatial and social factors to generate significant computational patterns and to investigate the problem of community detection over location-based social networks. Specifically, GSG detection aims to extract all group-venue clusters, where users are similar to each other in the same group and they are located in a minimum covering circle (MCC) for which the radius is no greater than a distance threshold γ. Then, we present a GSGD algorithm following a three-step paradigm to enumerate all qualified GSGs in a large network. We propose effective optimization techniques to efficiently enumerate all communities in a network. Furthermore, we extend a significant GSG detection problem to top-k geo-social group (TkGSG) mining. Rather than extracting all qualified GSGs in a network, TkGSG aims to return k feasibility groups to guarantee the diversity. We prove the hardness of computing the TkGSGs. Nevertheless, we propose the effective greedy approach with a guaranteed approximation ratio of 1−1/e. Extensive empirical studies on real and synthetic networks show the superiority of our algorithm when compared with existing methods and demonstrate the effectiveness of our new community model and the efficiency of our optimization techniques.
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Yan, Yeqing, Zhigang Chen, Jia Wu, and Leilei Wang. "An Effective Data Transmission Algorithm Based on Social Relationships in Opportunistic Mobile Social Networks." Algorithms 11, no. 8 (August 14, 2018): 125. http://dx.doi.org/10.3390/a11080125.

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With the popularization of mobile communication equipment, human activities have an increasing impact on the structure of networks, and so the social characteristics of opportunistic networks become increasingly obvious. Opportunistic networks are increasingly used in social situations. However, existing routing algorithms are not suitable for opportunistic social networks, because traditional opportunistic network routing does not consider participation in human activities, which usually causes a high ratio of transmission delay and routing overhead. Therefore, this research proposes an effective data transmission algorithm based on social relationships (ESR), which considers the community characteristics of opportunistic mobile social networks. This work uses the idea of the faction to divide the nodes in the network into communities, reduces the number of inefficient nodes in the community, and performs another contraction of the structure. Simulation results show that the ESR algorithm, through community transmission, is not only faster and safer, but also has lower transmission delay and routing overhead compared with the spray and wait algorithm, SCR algorithm and the EMIST algorithm.
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Wang, Pingshui, Jianwen Zhu, and Qinjuan Ma. "Private Data Protection in Social Networks Based on Blockchain." International Journal of Advanced Networking and Applications 14, no. 04 (2023): 5549–55. http://dx.doi.org/10.35444/ijana.2023.14407.

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With the rapid development of big data and social networks, user data in social networks are facing huge risks of privacy leakage. It is urgent to establish a complete and effective method for protecting private data in social networks. Based on the problem of information leakage in social networks, classifies user privacy data, and constructs different privacy data protection schemes through blockchain time stamp recording data storage, hash function anonymous operation of data, asymmetric encryption and digital signature of sending information. The blockchain-based privacy data protection method in social networks can effectively solve the privacy leakage problem in social networks, and provide a reference for the research in the field of information security and social network security. This paper designs a new blockchain-based privacy data protection scheme for different privacy disclosure categories, which provides a new solution to the current privacy disclosure problem in social networks. However, the existing methods will consume a lot of computational power in the process of information interaction. The subsequent research will optimize the computational power of blockchain and try to build a better blockchain social network privacy data protection system.
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Dissertations / Theses on the topic "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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Books on the topic "Computational social networks"

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Nguyen, Hien T., and Vaclav Snasel, eds. Computational Social Networks. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42345-6.

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Abraham, Ajith, and Aboul-Ella Hassanien, eds. Computational Social Networks. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4048-1.

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Abraham, Ajith, ed. Computational Social Networks. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4051-1.

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Abraham, Ajith, ed. Computational Social Networks. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4054-2.

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Thai, My T., Nam P. Nguyen, and Huawei Shen, eds. Computational Social Networks. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21786-4.

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Mohaisen, David, and Ruoming Jin, eds. Computational Data and Social Networks. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-91434-9.

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Tagarelli, Andrea, and Hanghang Tong, eds. Computational Data and Social Networks. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34980-6.

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Chen, Xuemin, Arunabha Sen, Wei Wayne Li, and My T. Thai, eds. Computational Data and Social Networks. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04648-4.

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Chellappan, Sriram, Kim-Kwang Raymond Choo, and NhatHai Phan, eds. Computational Data and Social Networks. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66046-8.

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Dinh, Thang N., and Minming Li, eds. Computational Data and Social Networks. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26303-3.

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Book chapters on the topic "Computational social networks"

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Herbiet, Guillaume-Jean, and Pascal Bouvry. "Social Network Analysis Techniques for Social-Oriented Mobile Communication Networks." In Computational Social Networks, 51–80. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4048-1_3.

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Reinhardt, Wolfgang, Adrian Wilke, Matthias Moi, Hendrik Drachsler, and Peter Sloep. "Mining and Visualizing Research Networks Using the Artefact-Actor-Network Approach." In Computational Social Networks, 233–67. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4054-2_10.

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Salama, Mostafa, Mrutyunjaya Panda, Yomna Elbarawy, Aboul Ella Hassanien, and Ajith Abraham. "Computational Social Networks: Security and Privacy." In Computational Social Networks, 3–21. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4051-1_1.

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Huang, Jiaqing, Qingyuan Liu, Zhibin Lei, and Dah Ming Chiu. "Applications of Social Networks in Peer-to-Peer Networks." In Computational Social Networks, 301–27. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4048-1_12.

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Panda, Mrutyunjaya, Nashwa El-Bendary, Mostafa A. Salama, Aboul Ella Hassanien, and Ajith Abraham. "Computational Social Networks: Tools, Perspectives, and Challenges." In Computational Social Networks, 3–23. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4048-1_1.

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Kundu, Anirban. "Dynamic Web Prediction Using Asynchronous Mouse Activity." In Computational Social Networks, 257–80. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4048-1_10.

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Khodaparast, Ali Asghar, and Azade Kavianfar. "PPMN: A City Wide Reliable Public Wireless Mesh Network." In Computational Social Networks, 281–300. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4048-1_11.

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Pal, Arpan, Chirabrata Bhaumik, Priyanka Sinha, and Avik Ghose. "Intelligent Social Network of Devices." In Computational Social Networks, 329–48. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4048-1_13.

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Luo, Xun. "Social Network-Based Media Sharing in the Ubiquitous Environment: Technologies and Applications." In Computational Social Networks, 349–66. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4048-1_14.

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Geierhos, Michaela, and Mohamed Ebrahim. "Customer Interaction Management Goes Social: Getting Business Processes Plugged in Social Networks." In Computational Social Networks, 367–89. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4048-1_15.

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Conference papers on the topic "Computational social networks"

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Dragoni, Mauro. "Computational advertising in social networks." In SAC 2018: Symposium on Applied Computing. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3167132.3167324.

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Surma, Jerzy, Malgorzata Roszkiewcz, and Jacek Wojcik. "Towards Understanding Social Influence in On-Line Social Networks." In 2014 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2014. http://dx.doi.org/10.1109/csci.2014.132.

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Faghani, Mohammad Reza, and Hossein Saidi. "Social Networks' XSS Worms." In 2009 International Conference on Computational Science and Engineering. IEEE, 2009. http://dx.doi.org/10.1109/cse.2009.424.

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Lajmi, Sonia, Johann Stan, Hakim Hacid, Elöd Egyed-Zsigmond, and Pierre Maret. "Extended Social Tags: Identity Tags Meet Social Networks." In 2009 International Conference on Computational Science and Engineering. IEEE, 2009. http://dx.doi.org/10.1109/cse.2009.106.

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Hamamreh, Rushdi A., and Sameh Awad. "Tag Ranking Multi-agent Semantic Social Networks." In 2017 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2017. http://dx.doi.org/10.1109/csci.2017.156.

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Zhang, Huiqi, Ram Dantu, and Joao Cangussu. "Quantifying Reciprocity in Social Networks." In 2009 International Conference on Computational Science and Engineering. IEEE, 2009. http://dx.doi.org/10.1109/cse.2009.399.

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Smith, Marc, Derek L. Hansen, and Eric Gleave. "Analyzing Enterprise Social Media Networks." In 2009 International Conference on Computational Science and Engineering. IEEE, 2009. http://dx.doi.org/10.1109/cse.2009.468.

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"International Workshop on Computational Social Networks (IWCSN 2011)." In 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT). IEEE, 2011. http://dx.doi.org/10.1109/wi-iat.2011.301.

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Zhang, Xiaoqin, Vaishnavi Guduguntla, Kalyani Emani, Gaurav Kulkarni, and Pavan Kaparthi. "Get Smart on Information-Sharing in Social Networks." In 2018 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2018. http://dx.doi.org/10.1109/csci46756.2018.00242.

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Aimeur, Esma, Hicham Hage, and Sabrine Amri. "The Scourge of Online Deception in Social Networks." In 2018 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2018. http://dx.doi.org/10.1109/csci46756.2018.00244.

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Reports on the topic "Computational social networks"

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Berry, Nina M., Jessica Glicken Turnley, Julianne D. Smrcka, Teresa H. Ko, Timothy David Moy, and Benjamin C. Wu. Computational social network modeling of terrorist recruitment. Office of Scientific and Technical Information (OSTI), October 2004. http://dx.doi.org/10.2172/919633.

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African Open Science Platform Part 1: Landscape Study. Academy of Science of South Africa (ASSAf), 2019. http://dx.doi.org/10.17159/assaf.2019/0047.

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This report maps the African landscape of Open Science – with a focus on Open Data as a sub-set of Open Science. Data to inform the landscape study were collected through a variety of methods, including surveys, desk research, engagement with a community of practice, networking with stakeholders, participation in conferences, case study presentations, and workshops hosted. Although the majority of African countries (35 of 54) demonstrates commitment to science through its investment in research and development (R&D), academies of science, ministries of science and technology, policies, recognition of research, and participation in the Science Granting Councils Initiative (SGCI), the following countries demonstrate the highest commitment and political willingness to invest in science: Botswana, Ethiopia, Kenya, Senegal, South Africa, Tanzania, and Uganda. In addition to existing policies in Science, Technology and Innovation (STI), the following countries have made progress towards Open Data policies: Botswana, Kenya, Madagascar, Mauritius, South Africa and Uganda. Only two African countries (Kenya and South Africa) at this stage contribute 0.8% of its GDP (Gross Domestic Product) to R&D (Research and Development), which is the closest to the AU’s (African Union’s) suggested 1%. Countries such as Lesotho and Madagascar ranked as 0%, while the R&D expenditure for 24 African countries is unknown. In addition to this, science globally has become fully dependent on stable ICT (Information and Communication Technologies) infrastructure, which includes connectivity/bandwidth, high performance computing facilities and data services. This is especially applicable since countries globally are finding themselves in the midst of the 4th Industrial Revolution (4IR), which is not only “about” data, but which “is” data. According to an article1 by Alan Marcus (2015) (Senior Director, Head of Information Technology and Telecommunications Industries, World Economic Forum), “At its core, data represents a post-industrial opportunity. Its uses have unprecedented complexity, velocity and global reach. As digital communications become ubiquitous, data will rule in a world where nearly everyone and everything is connected in real time. That will require a highly reliable, secure and available infrastructure at its core, and innovation at the edge.” Every industry is affected as part of this revolution – also science. An important component of the digital transformation is “trust” – people must be able to trust that governments and all other industries (including the science sector), adequately handle and protect their data. This requires accountability on a global level, and digital industries must embrace the change and go for a higher standard of protection. “This will reassure consumers and citizens, benefitting the whole digital economy”, says Marcus. A stable and secure information and communication technologies (ICT) infrastructure – currently provided by the National Research and Education Networks (NRENs) – is key to advance collaboration in science. The AfricaConnect2 project (AfricaConnect (2012–2014) and AfricaConnect2 (2016–2018)) through establishing connectivity between National Research and Education Networks (NRENs), is planning to roll out AfricaConnect3 by the end of 2019. The concern however is that selected African governments (with the exception of a few countries such as South Africa, Mozambique, Ethiopia and others) have low awareness of the impact the Internet has today on all societal levels, how much ICT (and the 4th Industrial Revolution) have affected research, and the added value an NREN can bring to higher education and research in addressing the respective needs, which is far more complex than simply providing connectivity. Apart from more commitment and investment in R&D, African governments – to become and remain part of the 4th Industrial Revolution – have no option other than to acknowledge and commit to the role NRENs play in advancing science towards addressing the SDG (Sustainable Development Goals). For successful collaboration and direction, it is fundamental that policies within one country are aligned with one another. Alignment on continental level is crucial for the future Pan-African African Open Science Platform to be successful. Both the HIPSSA ((Harmonization of ICT Policies in Sub-Saharan Africa)3 project and WATRA (the West Africa Telecommunications Regulators Assembly)4, have made progress towards the regulation of the telecom sector, and in particular of bottlenecks which curb the development of competition among ISPs. A study under HIPSSA identified potential bottlenecks in access at an affordable price to the international capacity of submarine cables and suggested means and tools used by regulators to remedy them. Work on the recommended measures and making them operational continues in collaboration with WATRA. In addition to sufficient bandwidth and connectivity, high-performance computing facilities and services in support of data sharing are also required. The South African National Integrated Cyberinfrastructure System5 (NICIS) has made great progress in planning and setting up a cyberinfrastructure ecosystem in support of collaborative science and data sharing. The regional Southern African Development Community6 (SADC) Cyber-infrastructure Framework provides a valuable roadmap towards high-speed Internet, developing human capacity and skills in ICT technologies, high- performance computing and more. The following countries have been identified as having high-performance computing facilities, some as a result of the Square Kilometre Array7 (SKA) partnership: Botswana, Ghana, Kenya, Madagascar, Mozambique, Mauritius, Namibia, South Africa, Tunisia, and Zambia. More and more NRENs – especially the Level 6 NRENs 8 (Algeria, Egypt, Kenya, South Africa, and recently Zambia) – are exploring offering additional services; also in support of data sharing and transfer. The following NRENs already allow for running data-intensive applications and sharing of high-end computing assets, bio-modelling and computation on high-performance/ supercomputers: KENET (Kenya), TENET (South Africa), RENU (Uganda), ZAMREN (Zambia), EUN (Egypt) and ARN (Algeria). Fifteen higher education training institutions from eight African countries (Botswana, Benin, Kenya, Nigeria, Rwanda, South Africa, Sudan, and Tanzania) have been identified as offering formal courses on data science. In addition to formal degrees, a number of international short courses have been developed and free international online courses are also available as an option to build capacity and integrate as part of curricula. The small number of higher education or research intensive institutions offering data science is however insufficient, and there is a desperate need for more training in data science. The CODATA-RDA Schools of Research Data Science aim at addressing the continental need for foundational data skills across all disciplines, along with training conducted by The Carpentries 9 programme (specifically Data Carpentry 10 ). Thus far, CODATA-RDA schools in collaboration with AOSP, integrating content from Data Carpentry, were presented in Rwanda (in 2018), and during17-29 June 2019, in Ethiopia. Awareness regarding Open Science (including Open Data) is evident through the 12 Open Science-related Open Access/Open Data/Open Science declarations and agreements endorsed or signed by African governments; 200 Open Access journals from Africa registered on the Directory of Open Access Journals (DOAJ); 174 Open Access institutional research repositories registered on openDOAR (Directory of Open Access Repositories); 33 Open Access/Open Science policies registered on ROARMAP (Registry of Open Access Repository Mandates and Policies); 24 data repositories registered with the Registry of Data Repositories (re3data.org) (although the pilot project identified 66 research data repositories); and one data repository assigned the CoreTrustSeal. Although this is a start, far more needs to be done to align African data curation and research practices with global standards. Funding to conduct research remains a challenge. African researchers mostly fund their own research, and there are little incentives for them to make their research and accompanying data sets openly accessible. Funding and peer recognition, along with an enabling research environment conducive for research, are regarded as major incentives. The landscape report concludes with a number of concerns towards sharing research data openly, as well as challenges in terms of Open Data policy, ICT infrastructure supportive of data sharing, capacity building, lack of skills, and the need for incentives. Although great progress has been made in terms of Open Science and Open Data practices, more awareness needs to be created and further advocacy efforts are required for buy-in from African governments. A federated African Open Science Platform (AOSP) will not only encourage more collaboration among researchers in addressing the SDGs, but it will also benefit the many stakeholders identified as part of the pilot phase. The time is now, for governments in Africa, to acknowledge the important role of science in general, but specifically Open Science and Open Data, through developing and aligning the relevant policies, investing in an ICT infrastructure conducive for data sharing through committing funding to making NRENs financially sustainable, incentivising open research practices by scientists, and creating opportunities for more scientists and stakeholders across all disciplines to be trained in data management.
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