Stable Community Detection in Signed Social Networks

Abstract

Community detection is one of the most fundamental problems in social network analysis, while most existing research focuses on unsigned graphs. In real applications, social networks involve not only positive relationships but also negative ones. It is important to exploit the signed information to identify more stable communities. In this paper, we propose a novel model, named stable <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -core, to measure the stability of a community in signed graphs. The stable <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -core model not only emphasizes user engagement, but also eliminates unstable structures. We show that the problem of finding the maximum stable <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -core is NP-hard. To scale for large graphs, novel pruning strategies and searching methods are proposed. We conduct extensive experiments on 6 real-world signed networks to verify the efficiency and effectiveness of proposed model and techniques.