A game-theoretic framework to identify overlapping communities in social networks

Abstract

In this paper, we introduce a game-theoretic framework to address the community detection problem based on the structures of social networks. We formulate the dynamics of community formation as a strategic game called community formation game: Given an underlying social graph, we assume that each node is a selfish agent who selects communities to join or leave based on her own utility measurement. A community structure can be interpreted as an equilibrium of this game. We formulate the agents' utility by the combination of a gain function and a loss function. We allow each agent to select multiple communities, which naturally captures the concept of "overlapping communities". We propose a gain function based on the modularity concept introduced by Newman (Proc Natl Acad Sci 103(23): [8577][8578][8579][8580][8581][8582] 2006), and a simple loss function that reflects the intrinsic costs incurred when people join the communities. We conduct extensive experiments under this framework, and our results show that our algorithm is effective in identifying overlapping communities, and are often better then other algorithms we evaluated especially when many people Responsible editors:A game-theoretic framework 225 belong to multiple communities. To the best of our knowledge, this is the first time the community detection problem is addressed by a game-theoretic framework that considers community formation as the result of individual agents' rational behaviors.

How to identify experts in a community?

“…for advertising purposes). Chen et al (2010) examined the problem from game theoretical point of view, while Nicolas (2007) used the group identi cation framework to study such models. A straightforward question is how to extend the Top Candidate algorithm to handle multiple groups and how to solve the arising algorithmic and axiomatic di culties.…”

Cascade-based community detection

“…A recent survey by Xie et al [54] categorizes algorithms for overlapping community detection in various classes: methods based on clique percolation [41,42,30]; methods that extend the idea of label propagation [43] to produce overlapping communities [25,56,55]; agent-based and particles-based models [15,12]; methods based on local expansion and optimization [7,31,32,40]. But the two classes more relevant for our proposal are link partitioning methods and stochastic generative models, discussed next.…”

Dynamic Community Detection

“…However, it is losing popularity due to instability problems, explained below. This approach is notably used by Hopcroft et al ( 2004), Palla et al ( 2007), Wang et al ( 2008), Rosvall and Bergstrom ( 2010), Chen et al ( 2010), Greene et al ( 2010), Dhouioui and Akaichi(2014) and Ilhan and Oguducu (2015). Advantages The main advantage of this solution is to offer the possibility to reuse traditional community detection techniques without the necessity to modify them.…”

“…Communication datasets is another topic for which networks can be easily accessible. The Enron mail dataset has been studied in many publications (e.g., Falkowski et al ( 2008); Chen et al ( 2010)), as it is a relatively small network for which we have access to the complete evolution. Correlation can be seen between communities' birth and death and events happening at Enron, such as employees leaving the company.…”