Mining and Visualizing the Evolution of Subgroups in Social Networks

Abstract

A social network consists of people who interact in some way such as members of online communities sharing information via the WWW. To learn more about how to facilitate community building e.g. in organizations, it is important to analyze the interaction behavior of their members over time. So far, many tools have been provided that allow for the analysis of static networks and some for the temporal analysis of networks -however only on the vertex and edge level. In this paper we propose two approaches to analyze the evolution of two different types of online communities on the level of subgroups: The first method consists of statistical analyses and visualizations that allow for an interactive analysis of subgroup evolutions in communities that exhibit a rather membership structure. The second method is designed for the detection of communities in an environment with highly fluctuating members. For both methods, we discuss results of experiments with real data from an online student community.

Advances in dynamic temporal networks: Understanding the temporal dynamics of complex adaptive networks

“…Partly as a response to these current challenges, several approaches and tools were introduced more recently [18,25,40]. These are mostly isolated works, however, focusing on special problems.…”

Tracking Communities in Dynamic Social Networks

“…[11] proposed to identify communities by graph coloring; however, their framework assumes that the observed network at each time step is a disjoint union of cliques, whereas we target the more general case where the observed network can be an arbitrary graph. [3] proposed a method for tracking the evolution of communities that applies to the general case of arbitrary graphs. The method involves first performing ordinary community detection on time snapshots of the network by maximizing modularity.…”

Analyzing the Dynamics of Communication in Online Social Networks

“…Properties of social phenomenon, including communication, with respect to individuals, groups and networks have been studied immensely in the past to guide a wide range of problems, ranging from information diffusion (Gruhl et al, 2004;Song et al, 2007;Stewart et al, 2007), trust propagation (Gyöngyi et al, 2004), community detection (Chin and Chignell, 2007;Du et al, 2007;Falkowski et al, 2006;Lin et al, 2007;Zhou et al, 2006), social capital quantification and community-centric search (Almeida and Almeida, 2004;Balfe and Smyth, 2004;Boydell and Smyth, 2006;Coyle and Smyth, 2007), prediction of social collaboration (Fisher and Dourish, 2004;McDonald, 2003;Ohira et al, 2005), mining user behavior , expertise modeling (D'Amore, 2004) and analyzing predictive power of social communication (Gruhl et al, 2005). In this section we provide a detailed overview of the realm of prior work corresponding to (a) social network characterization at different granularities-individuals, groups and networks, (b) analysis of communication properties in social networks and (c) the evaluation of the dynamics of these characterizations and properties.…”