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

Advances to network analysis theories and methods with applications in social, organizational, and crisis settings

This dissertation proposes several solutions to the advancement of network analysis theories and methods with specific applications in the domains of social, organizational, and crisis scenarios. The field of network analysis has attracted interest from scholars coming from a wide range of disciplines as it provides valuable theoretical and methodological toolkits to investigate complex systems of social relations. Furthermore, network theories and methods can examine dynamics present at multiple levels of analysis, from individual- to global-levels. As a result, network analysis has been applied to various contexts of social science research such as social interactions, organizational communication, and crisis response collaboration. In this thesis, I present substantive insights into the application of several network analysis theories and applications to the (1) social, (2) organizational, and (3) crisis response settings. For the context of social interactions, I expand structural balance evaluation to signed and directed networks, and apply this approach to examine 12 social networks. For the context of organizational communication, I demonstrate the application of multilevel modeling for egocentric networks to examine factors associated with the formation of interdisciplinary ties in a scientific organization. In addition, I leverage an extended version of structural balance evaluation for signed and directed networks to examine the sources of tension present in three organizational networks. Third, I provide a case study of response dynamics during the 2010 Haiti earthquake by examining collaboration networks prescribed by national guidelines for response, and interaction networks of the actual collaborations that took place during the earthquake response. Altogether, this work contributes to the growing literature on the theories and applications of network analysis to real-world social networks. In particular, the study designs and findings developed in this thesis can provide a framework for network-based studies from many domains of interest, that includes components of network theories and methods that can help explain the social mechanisms involved in tie formation

Signed Networks in Social Media

Relations between users on social media sites often reflect a mixture of positive (friendly) and negative (antagonistic) interactions. In contrast to the bulk of research on social networks that has focused almost exclusively on positive interpretations of links between people, we study how the interplay between positive and negative relationships affects the structure of on-line social networks. We connect our analyses to theories of signed networks from social psychology. We find that the classical theory of structural balance tends to capture certain common patterns of interaction, but that it is also at odds with some of the fundamental phenomena we observe --- particularly related to the evolving, directed nature of these on-line networks. We then develop an alternate theory of status that better explains the observed edge signs and provides insights into the underlying social mechanisms. Our work provides one of the first large-scale evaluations of theories of signed networks using on-line datasets, as well as providing a perspective for reasoning about social media sites

A Model of Consistent Node Types in Signed Directed Social Networks

Signed directed social networks, in which the relationships between users can be either positive (indicating relations such as trust) or negative (indicating relations such as distrust), are increasingly common. Thus the interplay between positive and negative relationships in such networks has become an important research topic. Most recent investigations focus upon edge sign inference using structural balance theory or social status theory. Neither of these two theories, however, can explain an observed edge sign well when the two nodes connected by this edge do not share a common neighbor (e.g., common friend). In this paper we develop a novel approach to handle this situation by applying a new model for node types. Initially, we analyze the local node structure in a fully observed signed directed network, inferring underlying node types. The sign of an edge between two nodes must be consistent with their types; this explains edge signs well even when there are no common neighbors. We show, moreover, that our approach can be extended to incorporate directed triads, when they exist, just as in models based upon structural balance or social status theory. We compute Bayesian node types within empirical studies based upon partially observed Wikipedia, Slashdot, and Epinions networks in which the largest network (Epinions) has 119K nodes and 841K edges. Our approach yields better performance than state-of-the-art approaches for these three signed directed networks.Comment: To appear in the IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (ASONAM), 201