A no self-edge stochastic block model and a heuristic algorithm for balanced anti-community detection in networks

Abstract

Many real-world networks own the characteristic of anti-community structure, i.e. disassortative structure, where nodes share no or few connections inside their groups but most of their connections outside. Detecting anti-community structure can explore negative relations among objects. However, the structures output by the existing algorithms are unbalanced, leading to no or few negative relations to be explored in some groups. Stochastic block models are promising methods for exploring disassortative structures in networks, but their results are highly dependent on the observed structure of a network. In this paper, we first improve the classic stochastic block model and propose a No sElf-edge Stochastic blOck Model (NESOM) for anti-community structure. NESOM considers the edges inside and among groups, respectively, and evolves a new objective function for evaluating anti-community structure. And then, a new heuristic algorithm NESOM-AC is proposed for balanced anti-community detection, which consists of three stages: creation of initial structure, decomposition of redundant group, and adjustment of group membership. Inspired by NESOM, we finally develop a new synthetic benchmark NESOM-Net for performance comparison. Experimental results on NESOM-Net with up to 100,000 nodes and 16 real-world networks demonstrate the effectiveness of NESOM-AC in anti-community detection when compared with five state-of-the-art algorithms.