Applications of Structural Balance in Signed Social Networks

We present measures, models and link prediction algorithms based on the structural balance in signed social networks. Certain social networks contain, in addition to the usual 'friend' links, 'enemy' links. These networks are called signed social networks. A classical and major concept for signed social networks is that of structural balance, i.e., the tendency of triangles to be 'balanced' towards including an even number of negative edges, such as friend-friend-friend and friend-enemy-enemy triangles. In this article, we introduce several new signed network analysis methods that exploit structural balance for measuring partial balance, for finding communities of people based on balance, for drawing signed social networks, and for solving the problem of link prediction. Notably, the introduced methods are based on the signed graph Laplacian and on the concept of signed resistance distances. We evaluate our methods on a collection of four signed social network datasets.Comment: 37 page

Communities in Networks

We survey some of the concepts, methods, and applications of community detection, which has become an increasingly important area of network science. To help ease newcomers into the field, we provide a guide to available methodology and open problems, and discuss why scientists from diverse backgrounds are interested in these problems. As a running theme, we emphasize the connections of community detection to problems in statistical physics and computational optimization.Comment: survey/review article on community structure in networks; published version is available at http://people.maths.ox.ac.uk/~porterm/papers/comnotices.pd

An evaluation of identity in online social networking: distinguishing fact from fiction

Online social networks are understood to replicate the real life connections between people. As the technology matures, more people are joining social networking communities such as MySpace (www.myspace.com) and Facebook (www.facebook.com). These online communities provide the opportunity for individuals to present themselves and maintain social interactions through their profiles. Such traces in profiles can be used as evidence in deciding the level of trust with which to imbue individuals in making access control decisions. However, online profiles have serious implications over the reality of identity disclosure. There are many reasons why someone may choose not to reveal their true self, which sometimes leads to misidentification or deception. On one hand, the structure of online profiles allows anonymity, which gives users the opportunity to create a persona that may not represent their true identity. On the other hand, we often play multiple identities in different contexts where such behaviour is acceptable. However, realizing the context for each identity representation depends on the individual. As a result, some represented identities will be essentially real, if edited for public view, some will be disguised, and others will be fictitious or humorous. The millions of social network profiles, and billions of connections between them, make it difficult to formalize an automated approach to differentiate fact from fiction in online self-described identities. How can we be sure with whom we are interacting, and whether these individuals or groups are being truthful with the online identities they present to the rest of the community? What tools and techniques can be used to gather, organize, and explore the available data for informing the level of honesty that should be entrusted to an individual? Can we verify the validity of the identity automatically, based on the available information online? We aim to evaluate identity representation online and examine how identity can be verified in a less trusted online community. We propose a personality classifier model to identify a user‟s personality (such as expressive, valid, active, positive, popular, sociable and traceable) using traces of 2.2 million profile features collected from MySpace. We use data mining techniques and social network analysis to extract significant patterns in the data and network structure, and improve the classifier during the cycle of development. We evaluate our classifier model on profiles with known identities such as „real‟ and „fake‟. Our results indicate that by utilizing people‟s online, self-reported information, personality, and their network of friends and interactions, we are able to provide evidence for validating the type of identity in a manner that is both accurate and scalable

Understanding and designing for interactional privacy needs within social networking sites

"Interpersonal boundary regulation" is a way to optimize social interactions when sharing and connecting through Social Networking Sites (SNSs). The theoretical foundation of much of my research comes from Altman's work on privacy management in the physical world. Altman believed that "we should attempt to design responsive environments, which permit easy alternation between a state of separateness and a state of togetherness" (1975). In contrast, Mark Zuckerberg, Facebook's CEO, claims that sharing is the new "social norm" for Facebook's 800 million users (Facebook 2011), and it is Facebook's job to enable "frictionless sharing" (Matyszczyk 2010). My research focuses on reconciling this rift between social media sharing and privacy by examining interpersonal boundary regulation within SNSs as a means to align privacy needs with social networking goals. To do this, I performed an in-depth feature-oriented domain analysis (Kang, Cohen et al. 1990) across five popular SNS interfaces and 21 SNS user interviews to understand boundary mechanisms unique to these environments and their associated challenges. From this, I created a taxonomy of different interpersonal boundaries users manage within their SNSs, identified interface features that directly supported these boundary mechanisms, and uncovered coping behaviors for when interface features were inadequate or inappropriately leveraged. By better understanding this dynamic, we can begin to build new interfaces to help support and possibly even correct some of the maladaptive social behaviors exhibited within SNSs. Finally, I conducted two empirical studies that quantitatively validated some of the relationships in my theoretical model of the interpersonal boundary regulation process within SNSs. Specifically, I examined the role of risk awareness, feature awareness, burden, and desired privacy level on SNS privacy behaviors. I also examined the relationship between privacy outcomes and SNS goals of connecting and sharing with others. Through this research, I show that boundary regulation allows SNS users to reap the benefits of social networking while simultaneously protecting their privacy

Trust networks for recommender systems

Recommender systems use information about their user’s profiles and relationships to suggest items that might be of interest to them. Recommenders that incorporate a social trust network among their users have the potential to make more personalized recommendations compared to traditional systems, provided they succeed in utilizing the additional (dis)trust information to their advantage. Such trust-enhanced recommenders consist of two main components: recommendation technologies and trust metrics (techniques which aim to estimate the trust between two unknown users.) We introduce a new bilattice-based model that considers trust and distrust as two different but dependent components, and study the accompanying trust metrics. Two of their key building blocks are trust propagation and aggregation. If user a wants to form an opinion about an unknown user x, a can contact one of his acquaintances, who can contact another one, etc., until a user is reached who is connected with x (propagation). Since a will often contact several persons, one also needs a mechanism to combine the trust scores that result from several propagation paths (aggregation). We introduce new fuzzy logic propagation operators and focus on the potential of OWA strategies and the effect of knowledge defects. Our experiments demonstrate that propagators that actively incorporate distrust are more accurate than standard approaches, and that new aggregators result in better predictions than purely bilattice-based operators. In the second part of the dissertation, we focus on the application of trust networks in recommender systems. After the introduction of a new detection measure for controversial items, we show that trust-based approaches are more effective than baselines. We also propose a new algorithm that achieves an immediate high coverage while the accuracy remains adequate. Furthermore, we also provide the first experimental study on the potential of distrust in a memory-based collaborative filtering recommendation process. Finally, we also study the user cold start problem; we propose to identify key figures in the network, and to suggest them as possible connection points for newcomers. Our experiments show that it is much more beneficial for a new user to connect to an identified key figure instead of making random connections

Identification of Influentials in virtual social network: an agent-based simulation model of social influence processes

Die zunehmende Virtualisierung von gesellschaftlichen Sozialstrukturen durch den Social Media Bereich und insbesondere durch die virtuellen sozialen Netzwerke stellt die Marketingforschung vor neue Herausforderungen. Aufgrund der technologischen Entwicklung des Web 2.0 entstehen für Konsumenten schnelle und einfache Kommunikations- und Interaktionsmöglichkeiten zum Erfahrungsaustausch über die Produkte und Dienstleistungen eines Unternehmens. Innerhalb eines virtuellen sozialen Netzwerkes existieren Influentials, die aufgrund ihrer kommunikativen Verhaltensweisen und der netzwerkstrukturellen Einbettung eine einzigartige soziale Beeinflussungsfähigkeit aufweisen. Für das Marketing der Unternehmen stellen das Verständnis über die sozialen Beeinflussungsprozesse und die Identifikation der Influentials die zentralen Erfolgsfaktoren dar, um die Konsumenteninteraktion im Sinne der Unternehmenszielsetzung zu beeinflussen. Bisherige Analyse- bzw. Identifikationsmethoden für diese Influentials vernachlässigen jedoch die bedeutsame interpersonelle Perspektive. Die netzwerkstrukturelle Einbettung der Konsumenten bzw. Individuen sowie deren Kommunikations- und Interaktionsprozesse untereinander führen zu einem dynamischen, nichtlinearen und komplexen Sozialsystem. Bei der Untersuchung dieser Dynamiken stoßen traditionelle Analysemethoden der Marketingforschung an ihre Grenzen. Deshalb entwickelt der Verfasser ein agentenbasiertes Simulationsmodell, um das individuelle Konsumentenhalten als komplexes und dynamisches System abzubilden. Die Simulationsergebnisse deuten darauf hin, dass die Influentials weder über eine strukturell besonders bedeutsame Position innerhalb des Netzwerkes verfügen, noch eine erhöhte soziale Aktivität aufweisen. Die bisher verwendeten Verfahren der strukturellen sozialen Netzwerkanalyse und der sozialen Aktivitätsanalyse sind deshalb nur eingeschränkt zur Identifikation von Influentials geeignet. Aus einer interpersonellen Analyseperspektive zeigt sich, dass die Influentials eine besonders hohe wahrgenommene Glaubwürdigkeit aufweisen und das soziale Umfeld dieser Individuen durch eine hohe Empfänglichkeit für soziale Beeinflussungen gekennzeichnet ist. Die agentenbasierte Simulation erweitert somit das Verständnis über das sozial beeinflusste Konsumentenverhalten und liefert damit wertvolle Hinweise für die praxisnahe Identifikation von Influentials in einem virtuellen sozialen Netzwerk.Virtual social networking sites have become more and more popular over the last few years, attract millions of users worldwide and are growing exponentially. The increasing amount of virtually connected consumers leads to a social-driven information exchange about products, brands or services. Within virtual social networks, influentials can be considered as key users with high influence capabilities, unique communication patterns and important structural network positions. For marketers an understanding of social influence is key to benefit from consumer-to-consumer interaction and to address potential new customers by utilizing these influentials. So far, virtual social network analysis neglects interpersonal factors of influence as well as an individual consumer decision making perspective. The analysis of individual interaction and the lack of empirical data from virtual social networks require a research method, which models individual consumer behaviors as a complex and adaptive system. Therefore, the author develops an agent-based simulation model to explore and to investigate social influence processes by integrating perceived social activity, perceived structural positions and interpersonal relationship characteristics with an individual decision making perspective. Simulation results indicate that important members in virtual social networks are inadequately identified either through structural network or activity analysis respectively. Hence, these methods are less appropriate to identify influentials within a virtual social network. The interpersonal analysis of the social influence processes shows that influentials are characterized by a high perceived credibility. Moreover, the social contacts of the influentials are highly susceptible for social influences. The agent-based simulation model provides a deeper understanding of social influence processes in virtual social networks and serves marketers as a superior opportunity for identifying socially influential network members