Dissertations / Theses on the topic 'Online social networks – Mathematical models'

Web-based social networks have become one of the most popular applications on the Internet in recent years. However, most of the social networks rely on some simplistic trust models to manage trust information of the users, which can cause problems ranging from unsatisfied user experience to exposure to malicious users. This thesis proposes a gravity-based trust model to enhance the aggregation of personal trust information into a subjective reputation system. This new model maps all users on the social network into n-dimensional Euclidean spaces based on their direct trust information, and creates a trust social neighborhood for each user. The reputation of a target user is determined by applying gravity model to the information from both target's and observer's trust social neighborhood. A prototype of this trust model is implemented in order to evaluate the effects of varying different parameters of the gravity-based trust model.

Online social and information networks, like Facebook and Twitter, exploit the influence of neighbors to achieve effective information sharing and spreading. The process that information is spread via the connected nodes in social and information networks is referred to as diffusion. In the literature, a number of diffusion models have been proposed for different applications like influential user identification and personalized recommendation. However, comprehensive studies to discover the hidden diffusion mechanisms governing the information diffusion using the data-driven paradigm are still lacking. This thesis research aims to design novel diffusion models with the structural and behaviorable dependency of neighboring nodes for representing social networks, and to develop computational algorithms to infer the diffusion models as well as the underlying diffusion mechanisms based on information cascades observed in real social networks. By incorporating structural dependency and diversity of node neighborhood into a widely used diffusion model called Independent Cascade (IC) Model, we first propose a component-based diffusion model where the influence of parent nodes is exerted via connected components. Instead of estimating the node-based diffusion probabilities as in the IC Model, component-based diffusion probabilities are estimated using an expectation maximization (EM) algorithm derived under a Bayesian framework. Also, a newly derived structural diversity measure namely dynamic effective size is proposed for quantifying the dynamic information redundancy within each parent component. The component-based diffusion model suggests that node connectivity is a good proxy to quantify how a node's activation behavior is affected by its node neighborhood. To model directly the behavioral dependency of node neighborhood, we then propose a co-activation pattern based diffusion model by integrating the latent class model into the IC Model where the co-activation patterns of parent nodes form the latent classes for each node. Both the co-activation patterns and the corresponding pattern-based diffusion probabilities are inferred using a two-level EM algorithm. As compared to the component-based diffusion model, the inferred co-activation patterns can be interpreted as the soft parent components, providing insights on how each node is influenced by its neighbors as reflected by the observed cascade data. With the motivation to discover a common set of the over-represented temporal activation patterns (motifs) characterizing the overall diffusion in a social network, we further propose a motif-based diffusion model. By considering the temporal ordering of the parent activations and the social roles estimated for each node, each temporal activation motif is represented using a Markov chain with the social roles being its states. Again, a two-level EM algorithm is proposed to infer both the temporal activation motifs and the corresponding diffusion network simultaneously. The inferred activation motifs can be interpreted as the underlying diffusion mechanisms characterizing the diffusion happening in the social network. Extensive experiments have been carried out to evaluate the performance of all the proposed diffusion models using both synthetic and real data. The results obtained and presented in the thesis demonstrate the effectiveness of the proposed models. In addition, we discuss in detail how to interpret the inferred co-activation patterns and interaction motifs as the diffusion mechanisms under the context of different real social network data sets.

Thesis (MSc)--Stellenbosch University, 2011
ENGLISH ABSTRACT: With billions of current internet users interacting through social networks, the need has arisen to analyze the structure of these networks. Many authors have proposed random graph models for social networks in an attempt to understand and reproduce the dynamics that govern social network development. This thesis proposes a random graph model that generates social networks using a community-based approach, in which users’ affiliations to communities are explicitly modeled and then translated into a social network. Our approach explicitly models the tendency of communities to overlap, and also proposes a method for determining the probability of two users being connected based on their levels of commitment to the communities they both belong to. Previous community-based models do not incorporate community overlap, and assume mutual members of any community are automatically connected. We provide a method for fitting our model to real-world social networks and demonstrate the effectiveness of our approach in reproducing real-world social network characteristics by investigating its fit on two data sets of current online social networks. The results verify that our proposed model is promising: it is the first community-based model that can accurately reproduce a variety of important social network characteristics, namely average separation, clustering, degree distribution, transitivity and network densification, simultaneously.
AFRIKAANSE OPSOMMING: Met biljoene huidige internet-gebruikers wat deesdae met behulp van aanlyn sosiale netwerke kommunikeer, het die analise van hierdie netwerke in die navorsingsgemeenskap toegeneem. Navorsers het al verskeie toevalsgrafiekmodelle vir sosiale netwerke voorgestel in ’n poging om die dinamika van die ontwikkeling van dié netwerke beter te verstaan en te dupliseer. In hierdie tesis word ’n nuwe toevalsgrafiekmodel vir sosiale netwerke voorgestel wat ’n gemeenskapsgebaseerde benadering volg, deurdat gebruikers se verbintenisse aan gemeenskappe eksplisiet gemodelleer word, en dié gemeenskapsmodel dan in ’n sosiale netwerk omskep word. Ons metode modelleer uitdruklik die geneigdheid van gemeenskappe om te oorvleuel, en verskaf ’n metode waardeur die waarskynlikheid van vriendskap tussen twee gebruikers bepaal kan word, op grond van hulle toewyding aan hulle wedersydse gemeenskappe. Vorige modelle inkorporeer nie gemeenskapsoorvleueling nie, en aanvaar ook dat alle lede van dieselfde gemeenskap vriende sal wees. Ons verskaf ’n metode om ons model se parameters te pas op sosiale netwerk datastelle en vertoon die vermoë van ons model om eienskappe van sosiale netwerke te dupliseer. Die resultate van ons model lyk belowend: dit is die eerste gemeenskapsgebaseerde model wat gelyktydig ’n belangrike verskeidenheid van sosiale netwerk eienskappe, naamlik gemiddelde skeidingsafstand, samedromming, graadverdeling, transitiwiteit en netwerksverdigting, akkuraat kan weerspieël.

Multi Agent Systems (MAS) are computerised systems composed of autonomous software agents that interact to solve complex problems. Within a MAS, agents require some mechanism to coordinate their activities. In the MAS literature, norms have been widely used to coordinate agents’ activities. Thus, given a MAS, a major research challenge is how to synthesise a normative system, namely a collection of norms, which supports its agents’ coordination. This dissertation focuses on the automated synthesis of norms for open Multi- Agent Systems. In an open MAS, the agent population may change along time, agents may be developed by third parties and their behaviours are not known beforehand. These particular conditions make specially challenging to synthesise a normative system to govern an open MAS. The MAS literature has mainly investigated two general approaches to norm synthesis: off-line design, and on-line synthesis. The first approach aims at synthesising a normative system at design time. With this aim, it assumes that the MAS state space is known at design time and does not change at runtime. This goes against the nature of open MAS, and thus off-line design is not appropriate to synthesise their norms. Alternatively, on-line norm synthesis considers that norms are synthesised at runtime. Most on-line synthesis research has focused on norm emergence, which considers that agents synthesise their own norms, thus assuming that they have norm synthesis capabilities. Again, this cannot be assumed in open MAS. Against this background, this dissertation introduces a whole computational framework to perform on-line norm synthesis for open Multi-Agent Systems. Firstly, this framework provides a computational model to synthesise norms for a MAS at runtime. Such computational model requires neither knowledge about agents’ behaviours beforehand nor their participation in the norm synthesis pro- cess. Instead, it considers a regulatory entity that observes agents’ interactions at runtime, identifying situations that are undesirable for coordination to sub- sequently synthesise norms that regulate these situations. Our computational model has been conceived to be of general purpose so that it can be employed to synthesise norms in a wide range of application domains by providing little domain-dependent information. Secondly, our framework provides an abstract architecture to implement such regulatory entity (the so-called Norm Synthesis Machine), which observes a MAS and executes a synthesis strategy to synthe- sise norms. Thirdly, our framework encompasses a family of norm synthesis strategies intended to be executed by the Norm Synthesis Machine. Overall, this family of strategies supports multi-objective on-line norm synthesis Our first synthesis strategy, the so-called base, aims at synthesising effective normative systems that successfully avoid situations that are undesirable for a MAS’ coordination. Then, two further strategies (called iron and simon) go beyond effectiveness and also consider compactness as a norm synthesis goal. iron and simon take alternative approaches to synthesise compact normative systems that, in addition to effectively achieve coordination, are as synthetic as possible. This allows them to reduce agents’ computational efforts when reasoning about norms. A fourth strategy, the so-called lion, goes beyond effectiveness and compactness to also consider liberality as a synthesis goal. lion aims at synthesising normative systems that are effective and compact while preserving agents’ freedom to the greatest possible extent. Our final strategy is desmon, which is capable of synthesising norms by considering different degrees of reactivity. desmon allows to adjust the amount of information that is required to decide whether a norm must be included in a normative system or not. Thus, desmon can synthesise norms either by being reactive (i.e., by considering little information), or by being more deliberative (by employing more information). We provide empirical evaluations of our norm synthesis strategies in two application domains: a road traffic domain, and an on-line community domain. In this former domain, we employ these strategies to synthesise effective, compact and liberal normative systems that successfully avoid collisions between cars. In the latter domain, our strategies synthesise normative systems based on users’ complaints about inappropriate contents. In this way, our strategies implement a regulatory approach that synthesises norms when there is enough user consensus about the need for norms. Overall, this thesis advances in the state of the art in norm synthesis by providing a novel computational model, an abstract architecture and a family of strategies for on-line norm synthesis for open Multi-Agent Systems.
Els sistemes Multi-Agent (MAS) són sistemes computeritzats composats d’agents autònoms que interaccionen per resoldre problemes complexos. A un MAS, els agents requereixen algun mecanisme per a coordinar les seves activitats. A la literatura en Sistemes Multi-Agent, les normes han estat àmpliament utilitzades per coordinar les activitats dels agents. Per tant, donat un MAS, un dels majors reptes d’investigació és el de sintetizar el sistema normatiu, és a dir, la col·lecció de normes, que suporti la coordinació dels agents. Aquesta tesi es centra en la síntesi automàtica de normes per sistemes Multi-Agent oberts. A un MAS obert, la població d’agents pot canviar amb el temps, els agents poden ésser desenvolupats per terceres parts, i els comportaments dels agents són desconeguts per endavant. Aquestes condicions particulars fan especialment complicat sintetizar el sistema normatiu que reguli un sistema Multi-Agent obert. En general, la literatura en Sistemes Multi-Agent ha investigat dues aproximacions a la síntesi de normes: disseny off-line, i síntesi on-line. La primera aproximació consisteix a sintetizar un sistema normatiu en temps de disseny. Amb aquest propòsit, aquesta aproximació assumeix que l’espai d’estats d’un MAS és conegut en temps de disseny i no canvia en temps d’execució. Això va contra la natura dels sistemes Multi-Agent oberts, i per tant el disseny off-line no és apropiat per a sintetitzar les seves normes. Com a alternativa, la síntesi on-line considera que les normes són sintetizades en temps d’execució. La majoria de recerca en síntesi on-line s’ha centrat en la emergència de normes, que considera que els agents sintetizen les seves pròpies normes, per tant assumint que tenen la capacitat de sintetitzar-les. Aquestes condicions tampoc no es poden assumir en un MAS obert. Donat això, aquesta tesi introdueix un marc computacional per la síntesi on-line de normes en sistemes Multi-Agent oberts. Primer, aquest marc proveeix un model computacional per sintetizar normes per un MAS en temps d’execució. Aquest model computacional no requereix ni coneixement sobre els comportaments dels agents per endavant ni la seva participación en la síntesi de normes. En canvi, considera que una entitat reguladora observa les interaccions dels agents en temps d’execució, identificant situacions indesitjades per la coordinació i sintetizant normes que regulen aquestes situacions. El nostre model computacional ha estat dissenyat per a ésser de propòsit general per tal que pugui ser utilitzat a la síntesi de normes en un ampli ventall de dominis d’aplicació proporcionant només información clau sobre el domini. Segon, el nostre marc proveeix una arquitectura abstracta per implementar aquesta entitat reguladora, anomenada Màquina de Síntesi, que observa un MAS en temps d’execució i executa una estratègia de síntesi que s’encarrega de sintetizar normes. Tercer, el nostre marc incorpora una familia d’estratègies de síntesi destinades a ésser executades per una màquina de síntesi. En general, aquesta familia d’estratègies soporta la síntesi multi-objectiu i on-line de normes. La nostra primera estratègia, anomenada BASE, està dissenyada per sintetitzar sistemes normatius eficaços que evitin de manera satisfactòria situacions indesitjades per la coordinació d’un sistema Multi-Agent. Després, dues estratègies de síntesi, anomenades IRON i SIMON, van més enllà de la eficàcia i també consideren la compacitat com a objectiu de síntesi. IRON i SIMON prenen aproximacions alternatives a la síntesi de sistemes normatius compactes que, a més d’aconseguir la coordinació de manera efectiva, siguin tant sintètics com fos possible. Això permet a aquestes estratègies reduir els esforços computacionals dels agents a l’hora de raonar sobre les normes. Una quarta estratègia, anomenada LION, va més enllà de la eficàcia i la compacitat per considerar també la liberalitat com a objectiu de síntesi. Lion sintetitza sistemes normatius que són eficaços i compactes mentre preserven la llibertat dels agents tant com sigui possible. La nostra última estratègia és desmon, que és capaç de sintetizar normes considerant diferents graus de reactivitat. desmon permet ajustar la quantitat d’informació necessària per decidir si una norma cal que sigui o no inclosa a un sistema normatiu. DESMON pot sintetizar normes essent reactiu (considerant poca informació), o essent més deliberatiu (considerant més informació). En aquesta tesi presentem avaluacions empíriques de les nostres estratègies de síntesi en dos dominis d’aplicació: el domini del tràfic, i el domini de les comunitats on-line. En aquest primer domini, utilitzem les nostres estratègies per a sintetizar sistemes normatius eficaços, compactes i liberals que eviten colisions entre cotxes. Al segon domini, les nostres estratègies sintetizen sistemes normatius basant-se en les queixes dels usuaris de la comunitat sobre continguts inapropiats. D’aquesta manera, les nostres estratègies implementen un mecanisme de regulació que sintetiza normes quan hi ha suficient consens entre els usuaris sobre la necessitat de normes. Aquesta tesi avança en l’estat de l’art en síntesi de normes al proporcionar un novedós model computacional, una arquitectura abstracta i una familia d’estratègies per la síntesi on-line de normes per sistemes Multi-Agent oberts.

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

Thesis (Ph.D.)--George Mason University, 2008.
Vita: p. 214-215. Thesis director: Edward J. Wegman, Yasmin H. Said Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computational Sciences and Informatics. Title from PDF t.p. (viewed Mar. 9, 2009). Includes bibliographical references (p. 209-213). Also issued in print.

Traditional Public Health decision-support can benefit from the Web and social media revolution. This dissertation presents approaches to mining social media benefiting public health epidemiology. Through discovery and analysis of trends in Influenza related blogs, a correlation to Centers for Disease Control and Prevention (CDC) influenza-like-illness patient reporting at sentinel health-care providers is verified. A second approach considers personal beliefs of vaccination in social media. A vaccine for human papillomavirus (HPV) was approved by the Food and Drug Administration in May 2006. The virus is present in nearly all cervical cancers and implicated in many throat and oral cancers. Results from automatic sentiment classification of HPV vaccination beliefs are presented which will enable more accurate prediction of the vaccine's population-level impact. Two epidemic models are introduced that embody the intimate social networks related to HPV transmission. Ultimately, aggregating these methodologies with epidemic and social network modeling facilitate effective development of strategies for targeted interventions.

Previous studies targeting youth participation in physical activity have argued that self-motivation is the main key to increasing participation. However, few studies have focused specifically on the role of structural factors in prompting youth participation in physical activity. The structure may include people, and institutions that are introducing, providing and facilitating physical activity to youth. Therefore, this study focuses on the role of the structure surrounding youth. The study takes youth in Saudi Arabia aged 15-24 as its subjects in order to examine the use of three online social networks (OSNs), i.e., Facebook, Twitter, and Instagram, for communication and exchange of resources and the influence on participation of key decision makers such as home (parents and siblings), school (Ministry of Education and PE teachers), physical activity and sports clubs (General Authority of Sports [GAS]), and friends. The study uses mixed methods and follows the social network structural theory to examine how the exchange of resources (e.g., information, emotional support, financial support, and facilities and services) takes place between agent and structure. The main findings are that the structure plays a role in influencing participation among Saudi youth. Friends are of great influence, as they occupy the longest hours of youth time both at school, where friends interact in person, and outside of school, where friends communicate through OSNs. An Islamic and conservative society prevails in Saudi Arabia, where 99 per cent of the population is Muslim. Therefore, in addition to the structural factors noted above, religion is also investigated. Indeed, Islam drives motivation in this large conservative group as individuals learn to obey and implement the religious advice and Islamic teachings of the prophet Mohammed (peace be upon him), including those messages with relation to becoming a healthy and strong Muslim. Finally, the study also focuses on the participation of Saudi female youth in physical activity. Due to cultural reasons preventing women from participating in physical activity as freely and equally as their male peers in the country, Saudi Arabia has seen an increasing percentage of obese women. The main aim of this research is to understand the relationship between agency and structure and thereby to identify the role of structure in increasing the participation of youth in physical activity. The research question (How do OSNs facilitate Saudi youth participation in physical activity?) investigates the relationship between agency and structure to delineate the pattern of information exchange regarding resources for involvement in physical activity. Through the use of mixed methods including face-to-face interviews, online survey and digital ethnography, the researcher investigates how youth social networks function both offline and online. The study concludes that decision makers in the field of physical activity participation in Saudi Arabia vary in their level of encouragement, influence and communication. Family members do not seem to communicate with youth via online platforms, but they do play a crucial role in offline social networks. Private institutions are becoming very active in OSNs, and public institutions are following the trend, albeit at a slower pace. The study shows that physical activity facilitators in Saudi Arabia are still failing to effectively reach youth and encourage them to participate in physical activity. Various policies need to be reviewed and enhanced if the public institutions do indeed want to reach more youth and benefit youth and the community, including the female youth, who make up more than half of the population. The study shows that the way to develop these policies is to communicate with youth via OSNs and to provide youth with more facilities, venues and services in the country that are suitable for both genders.

With the ubiquity of broadband, wireless and mobile networking and the diversity of user-driven social networks and social channels, we are entering an information age where people and vehicles are connected at all times, and information and influence are diffused continuously through not only traditional authoritative media such as news papers, TV and radio broadcasting, but also user-driven new channels for disseminating information and diffusing influence. Social network users and mobile travelers can influence and be influenced by the social and spatial connectivity that they share through an impressive array of social and spatial channels, ranging from friendship, activity, professional or social groups to spatial, location-aware, and mobility aware events. In this dissertation research, we argue that spatial alarms and activity-based social networks are two fundamentally new types of information and influence diffusion channels. Such new channels have the potential of enriching our professional experiences and our personal life quality in many unprecedented ways. For instance, spatial alarms enable people to share their experiences or disseminate certain points of interest by leaving location-dependent greetings, tips or graffiti and location dependent tour guide to their friends, colleagues and family members. Through social networks, people can influence their friends and colleagues by the activities they have engaged, such as reviews and blogs on certain events or products. More interestingly, the power of such spatial and social diffusion of information and influence can go far beyond our physical reach. People can utilize user-generated social and spatial channels as effective means to disseminate information and propagate influence to a much wider and possibly unknown range of audiences and recipients at any time and in any location. A fundamental challenge in embracing such new and exciting ways of information diffusion is to develop effective and scalable models and algorithms as enabling technology and building blocks. This dissertation research is dedicated towards this ultimate objective with three novel and unique contributions. First, we develop an activity driven and self-configurable social influence model and a suite of computational algorithms to compute and rank social network nodes in terms of activity-based influence diffusion over social network topologies. By activity driven we mean that the real impact of social influence and the speed of such influence propagation should be computed based on the type, the amount and the time window of the activities performed by a social network node in addition to its social connectivity (social network topology). By self-configurable we mean that the diffusion efficiency and effectiveness are dynamically adapted based on the settings and tunings of multiple spatial and social parameters such as diffusion context, diffusion location, diffusion rate, diffusion energy (heat), diffusion coverage and diffusion incentives (e.g., reward points), to name a few. We evaluate our approach through datasets collected from Facebook, Epinions, and DBLP datasets. Our experimental results show that our activity based social influence model outperforms existing topology-based social influence model in terms of effectiveness and quality with respect to influence ranking and influence coverage computation. Second, we further enhance our activity based social influence model along two dimensions. At first, we use a probabilistic diffusion model to capture the intrinsic properties of social influence such that nodes in a social network may have the choice of whether to participate in a social influence propagation process. We examine threshold based approach and independent probabilistic cascade based approach to determine whether a node is active or inactive in each round of influence diffusion. Secondly, we introduce incentives using multi-scale reward points, which are popularly used in many business settings. We then examine the effectiveness of reward points based incentives in stimulating the diffusion of social influences. We show that given a set of incentives, some active nodes may become more active whereas some inactive nodes may become active. Such dynamics changes the composition of the top-k influential nodes computed by activity-based social influence model. We make several interesting observations: First, popular users who are high degree nodes and have many friends are not necessarily influential in terms of spawning new activities or spreading ideas and information. Second, most influential users are more active in terms of their participation in the social activities and interactions with their friends in the social network. Third, multi-scale reward points based incentives can be effective to both inactive nodes and active nodes. Third, we introduce spatial alarms as the basic building blocks for location-dependent information sharing and influence diffusion. People can share and disseminate their location based experiences and points of interest to their friends and colleagues in the form of spatial alarms. Spatial alarms are triggered and delivered to the intended subscribers only when the subscribers move into the designated geographical vicinity of the spatial alarms, enabling delivering and sharing of relevant information and experience at the right location and the right time with the right subscribers. We studied how to use locality filters and subscriber filers to enhance the spatial alarm processing using traditional spatial indexing techniques. In addition, we develop a fast spatial alarm indexing structure and algorithms, called Mondrian Tree, and demonstrate that the Mondrian tree enabled spatial alarm system can significantly outperform existing spatial indexing based solutions such as R-tree, $k$-d tree, Quadtree. This dissertation consists of six chapters. The first chapter introduces the research hypothesis. We describe our activity-based social influence model in Chapter 2. Chapter 3 presents the probabilistic social influence model powered with rewards incentives. We introduce spatial alarms and the basic system architecture for spatial alarm processing in Chapter 4. We describe the design of our Mondrian tree index of spatial alarms and alarm free regions in Chapter 5. In Chapter 6 we conclude the dissertation with a summary of the unique research contributions and a list of open issues closely relevant to the research problems and solution approaches presented in this dissertation.

The landscape of world migration involves multiple interacting movements of people at various geographic scales, posing significant challenges to the dyadic-independence assumption underlying standard migration models. To account for emerging patterns of multilateral migration relationships, we represent world migration as a time-evolving, spatial network. The nodes in the World Migration Network (WMN) are countries located in geographic space, and the edges represent migratory movements for each decade from 1960-2000. In the first part of the thesis, we characterise the spatial network structure of the WMN, with a particular focus on detecting and mapping mesoscopic structures called 'communities' (i.e., sets of countries with denser migration connections internally than to the rest of the WMN). We employ a method for community detection that simultaneously accounts for multilateral migration, spatial constraints, time-dependence, and directionality in the WMN. We then introduce an approach for characterising local (intracommunity) and global (intercommunity) connectivity in the WMN. On this basis, we define a threefold typology that distinguishes 'cave', 'bi-regional', and 'bridging' communities. These are characterised with distinct migration patterns, spatial network structures, and temporal dynamics: cave communities are tightly-knit enduring structures that channel local migration between contiguous countries; bi-regional communities merge migration between two distinct geographic regions; bridging communities have hub-and-spoke dynamic structures that emerge from globe-spanning movements. Our results suggest that the WMN is neither a globally interconnected network nor reproducing geographic boundaries but involves heterogeneous patterns of global and local ('glocal') migration connectivity. We examine a set of relational, homophily, and spatial mechanisms that could have possibly generated the 'glocal' structure we observe. We found that communities of different types arise from significantly different mechanisms. Our results suggest that migration communities can have important implications for world migration, as different types of community structure provide distinct opportunities and constraints, thereby distinctively shaping future migration patterns.

Thesis (MSc)--Stellenbosch University, 2013.
ENGLISH ABSTRACT: The amount of information that users of social networks consume on a daily basis is steadily increasing. The resulting information overload is usually associated with a loss of control over the management of information sources, leaving users feeling overwhelmed. To address this problem, social networks have introduced tools with which users can organise the people in their networks. However, these tools do not integrate any automated processing. Twitter has lists that can be used to organise people in the network into topic-based groups. This feature is a powerful organisation tool that has two main obstacles to widespread user adoption: the initial setup time and continual curation. In this thesis, we investigate the problem of constructing topic-based Twitter lists. We identify two subproblems, an unsupervised and supervised task, that need to be considered when tackling this problem. These subproblems correspond to a clustering and classification approach that we evaluate on Twitter data sets. The clustering approach is evaluated using multiple representation techniques, similarity measures and clustering algorithms. We show that it is possible to incorporate a Twitter user’s social graph data into the clustering approach to find topic-based clusters. The classification approach is implemented, from a statistical relational learning perspective, with kLog. We show that kLog can use a user’s tweet content and social graph data to perform accurate topic-based classification. We conclude that it is feasible to construct useful topic-based Twitter lists with either approach.
AFRIKAANSE OPSOMMING: Die stroom van inligting wat sosiale-netwerk gebruikers op ’n daaglikse basis verwerk, is aan die groei. Vir baie gebruikers, skep hierdie oordosis inligting ’n gevoel dat hulle beheer oor hul inligtingsbronne verloor. As ’n oplossing, het sosiale-netwerke meganismes geïmplementeer waarmee gebruikers die inligting in hul netwerk kan bestuur. Hierdie meganismes is nie selfwerkend nie, maar kort toevoer van die gebruiker. Twitter het lyste geïmplementeer waarmee gebruikers ander mense in hul sosiale-netwerk kan groepeer. Lyste is ’n kragtige organiserings meganisme, maar tog vind grootskaal gebruik daarvan nie plaas nie. Gebruikers voel dat die opstelling te veel tyd in beslag neem en die onderhoud daarvan te veel moeite is. Hierdie tesis ondersoek die probleem om onderwerp-gerigte Twitter lyste te skep. Ons identisifeer twee subprobleme wat aangepak word deur ’n nie-toesig en ’n toesighoudende metode. Hierdie twee metodes hou verband met trosvorming en klassifikasie onderskeidelik. Ons evalueer beide die trosvorming en klassifikasie op twee Twitter datastelle. Die trosvorming metode word geëvalueer deur te kyk na verskillende voorstellingstegnieke, eendersheid maatstawwe en trosvorming algoritmes. Ons wys dat dit moontlik is om ’n gebruiker se Twitter netwerkdata in te sluit om onderwerp-gerigte groeperinge te vind. Die klassifikasie benadering word geïmplementeer met kLog, vanuit ’n statistiese relasionele leertoerie perspektief. Ons wys dat akkurate onderwerp-gerigte klassifikasie resultate verkry kan word met behulp van gebruikers se tweet-inhoud en sosiale-netwerk data. In beide gevalle wys ons dat dit moontlik is om onderwerp-gerigte Twitter lyste, met goeie resultate, te bou.

[Truncated abstract] Current literature about university teaching argues that online teaching requires online social learning based on social interaction to be effective. This implies a shift in pedagogy based on engagement and collaboration, instead of trying to reproduce face-to- face teaching, in online environments. However, when a university adopts an elearning platform (or Learning Management System, LMS), most teachers tend to reproduce their traditional teaching, delivering, through the LMS, educational material. This study explored factors which influence university teachers to adopt teaching models based on online social interaction (OSI) when an e-learning platform is used to complement undergraduate classroom teaching. Online teaching model adoption was considered in the framework of technology adoption and post-adoption behavior, i.e., adoption and use by individuals after an organization has adopted an ICT-based innovation (Jasperson, Carter, & Zmud, 2005). Behaviors were investigated using a model based on Ajzen's (1991) Theory of Planned Behavior (TPB). In total, 26 university teachers 15 from Australia and 11 from Italy holding undergraduate courses, were recruited. They responded to a semi-structured interview based on the TPB, built on purpose for this research. Teachers were divided into three different groups on the basis of their approach to online teaching, corresponding to three different levels of adoption of OSI. The three different online teaching models were:

Indiana University-Purdue University Indianapolis (IUPUI)
In today's fast paced world, people have become increasingly interested in online communication to facilitate their lives and make it faster. This goes on from simple social interactions to more advanced actions like shopping on the internet. The presence of such activities makes it crucial for people to use their common sense and judgment to process all this information and evaluate what/who they trust and what/whom they do not. This process would have been much easier if the number of people in such networks is really small and manageable. However, there are millions of users who are hooked online every day. This makes the person very overwhelmed with his trusting decision, especially when it comes to interacting with strangers over the internet, and/or buying personal items, especially expensive ones. Therefore, many trust models have been proposed by computer scientists trying to evaluate and manage the trust between users using different techniques and combining many factors. What these computer scientists basically do is coming up with mathematical formulas and models to express trust in online networks and capture its parameters. However, social scientists are the people better trained to deal with concepts related to human behaviors and their cognitive thinking such as trust. Thus, in order for computer scientists to support their ideas and get a better insight about how to direct their research, people like social scientists should contribute. With this in mind, we realized in our group work the importance of such contribution, so we came up with the idea of my research work. In my search, I tried to find how these social scientists think and tackle a dynamic notion like trust, so we can use their findings in order to enhance our work and trust model. Through the chapters, I will discuss an already developed trust model that uses measurement theory in modeling trust. I will refer back to this model and see how other social scientists dealt with some of the issues encountered by the model and its functionality. Some small experiments have been done to show and compare our results with social scientists results for the same matter. One of the most important and controversial points to be discussed from social scientists' point of view is whether trust is transitive or not. Other points to be discussed and supported by social scientists' research include aggregation, reputation, timing effects on trust, reciprocity, and experience effects on trust. Some of these points are classified into trust mapping categories and others are related to trust management or decision making stages. In sum, this work is a multidisciplinary study of trust whose overall goal is to enhance our work and results, as computer scientists.

Li, Ronghua = 社会网络挖掘的算法问题研究 / 李荣华.
Thesis (Ph.D.)--Chinese University of Hong Kong, 2013.
Includes bibliographical references (leaves 157-171).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Abstract also in Chinese.
Li, Ronghua = She hui wang luo wa jue de suan fa wen ti yan jiu / Li Ronghua.

Interaction event times observed on a social network provide valuable information for social scientists to gain insight into complex social dynamics that are challenging to understand. However, it can be difficult to accurately represent the heterogeneity in the data and to model the dependence structure in the network system. This requires flexible models that can capture the complicated dynamics and complex patterns. Point process models offer an elegant framework for modeling event time data. This dissertation concentrates on developing point process models and related diagnostic tools, with a real data application involving an animal behavior network. In this dissertation, we first propose a Markov-modulated Hawkes process (MMHP) model to capture the sporadic and bursty patterns often observed in event time data. A Bayesian inference procedure is developed to evaluate the likelihood by using a variational approximation and the forward-backward algorithm. The validity of the proposed model and associated estimation algorithms is demonstrated using synthetic data and the animal behavior data. Facilitated by the power of the MMHP model, we construct network point process models that can capture a social hierarchy structure by embedding nodes in a latent space that can represent the underlying social ranks. Our model provides a ranking method for social hierarchy studies and describes the dynamics of social hierarchy formation from a novel perspective – taking advantage of the detailed information available in event time data. We show that the network point process models appropriately captures the temporal dynamics and heterogeneity in the network event time data, by providing meaningful inferred rankings and by calibrating the accuracy of predictions with relevant measures of uncertainty. In addition to developing a sensible and flexible model for network event time data, the last part of this dissertation provides essential tools for diagnosing lack of fit issues for such models. We develop a systematic set of diagnostic tools and visualizations for point process models fitted to data in the dynamic network setting. By inspecting the structure of the residual process and Pearson residual on the network, we can validate whether a model adequately captures the temporal and network dependence structures in the observed data.

Operating in an industry traditionally reluctant to change, companies in the art market have felt the need to adapt to new communication and information technologies, currently concentrating efforts to make advertising and brand engagement effective. In this sense, social networks are gaining increasing importance as they allow companies to reach a large number of consumers while keeping the costs quite low, especially when other types of promotion platforms are considered. This thesis aims to compare different communication methods on Instagram by: (1) companies with different business models and same dimensions; and (2) companies with same business models and different dimensions, tending to explore their impact on followers’ engagement levels. For that a quantitative analysis was performed via 421 posts from a total of 9 companies on the social network. The results obtained from the sample, tend to point to certain patterns between the formed clusters, which indicates that there are effectively differences in the mode of operation of exclusively online or traditional companies as well as in relation to differences in the scope of operations. This study, when associated with others of a more general nature, will allow marketers from companies involved in the art market to draw conclusions regarding the way they intend to implement their communication strategies through the use of social networks, more specifically by making use of Instagram, drawing lessons from how the largest international companies perform.
Operando num setor tradicionalmente relutante a mudanças, as empresas envolvidas no mercado de arte sentiram a necessidade de se adaptar às novas tecnologias de comunicação e informação, concentrando esforços para tornar eficiente a publicidade e o envolvimento do cliente com a marca. Nesse sentido, as redes sociais têm vindo a ganhar cada vez mais importância por permitirem alcançar um grande número de consumidores mantendo os custos reduzidos, principalmente quando confrontadas com outros tipos de plataformas de promoção. Esta tese tem como objetivo comparar diferentes métodos de comunicação no Instagram por: (1) empresas com diferentes modelos de negócio e dimensões semelhantes; e (2) empresas com o mesmo modelo de negócio mas diferentes dimensões, tendendo a explorar o seu impacto nos níveis de envolvimento dos seguidores. Para tal, foi realizada uma análise quantitativa através de 421 postagens de um total de 9 empresas nessa rede social. Os resultados obtidos através da amostra, tendem a apontar para certos padrões de atuação entre os grupos formados, o que indica que existem diferenças no modo de operação das empresas exclusivamente online comparadas com as tradicionais, bem como em relação às empresas de âmbito internacional comparativamente às nacionais. Este estudo, quando associado a outros de âmbito mais geral, permitirá que profissionais de marketing de empresas presentes no mercado de arte tirem conclusões sobre a forma como pretendem implementar as suas estratégias de comunicação através da utilização de redes sociais, mais especificamente através do Instagram, podendo tirar ilações relativamente à forma como o fazem as maiores empresas internacionais.

This study has focused on the conditions of indigenous entrepreneurs of production in the urban informal sector. In that sense, it is a first of its kind. Eleven Maya entrepreneurs in the city of Quetzaltenango, Guatemala, in five different productive activities, were interviewed. In addition a control group of three Ladino entrepreneurs was established and some large formal enterprises were visited. Besides analyzing the general working situation of the Maya entrepreneurs, the study tested two hypotheses on ethnicity. The first put forward the assumption that Maya entrepreneurs use their ethnic network to promote their enterprises, the other that Maya entrepreneurs are active in certain activities of the informal sector and not in others, due to, for example, structural conditions in the ethnically stratified and segregated society of Guatemala. Neither of these hypotheses were substantiated by the data. However, while ethnic segregation was not observed among Ladino and Maya entrepreneurs of production, there is circumstantial evidence of a structural discrimination that forces many Mayans who do not succeed in establishing a productive enterprise, to try their luck in the less economically promising sector of commerce. Besides the ethnic aspects, the study gave conclusive evidence for answers to some of the questions directed towards the informal sector in general, among them, the question whether or not capital accumulation takes place and, eventually, to which extent. The annual capital accumulation among productive enterprises in the informal sector of the city of Quetzaltenango was modeled. The results indicate an accumulation per year of roughly $1.5 million. Recalculations with a sensible variation of some of the crucial assumptions, gave results within a band of $1.35 million - $1.65 million. The capital is accumulated by 258 enterprises, with four or more workers (including the owner), with a total work force of 1,320 workers, out of a total of 1879 enterprises of production. To this author's knowledge, no similar attempt of such an estimation has been reported in the literature before. The study offers calculations on the economic take-home earnings of some of the Maya entrepreneurs and identifies the mechanisms behind the entrepreneurial successes and failures. It concludes that it is necessary to distinguish between enterprises of production and enterprises of commerce due to their different natures. It presents data on the labor wages in the informal sector. It shows that the salaries are, first, closely related to the productivity of the individual worker, and, second, that, probably more often than not, they are tied to fluctuations in the demand of the market for the products of the enterprise. This means that the salary bracket within one economic activity may vary widely throughout the year. Other topics where the study offers new insight on entrepreneurial practice in the informal sector, are on lending conditions and the use of formal loans, on taxation, on the use of different management schemes and the potential of these, and on productivity and profitability within different economic activities. A list of the findings of the study is given at the end.
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