A degree-based block model and a local expansion optimization algorithm for anti-community detection in networks

Jiajing Zhu; Yongguo Liu; Changhong Yang; Wen Yang; Zhi Chen; Yun Zhang; Shangming Yang; Xindong Wu

doi:10.1371/journal.pone.0195226

A degree-based block model and a local expansion optimization algorithm for anti-community detection in networks

PLoS One. 2018 Apr 18;13(4):e0195226. doi: 10.1371/journal.pone.0195226. eCollection 2018.

Authors

Jiajing Zhu¹, Yongguo Liu¹, Changhong Yang², Wen Yang², Zhi Chen¹, Yun Zhang¹, Shangming Yang¹, Xindong Wu³

Affiliations

¹ Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
² Sichuan Center for Disease Control and Prevention, Chengdu, Sichuan, China.
³ School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, Louisiana, United States of America.

Abstract

Anti-community detection in networks can discover negative relations among objects. However, a few researches pay attention to detecting anti-community structure and they do not consider the node degree and most of them require high computational cost. Block models are promising methods for exploring modular regularities, but their results are highly dependent on the observed structure. In this paper, we first propose a Degree-based Block Model (DBM) for anti-community structure. DBM takes the node degree into consideration and evolves a new objective function Q(C) for evaluation. And then, a Local Expansion Optimization Algorithm (LEOA), which preferentially considers the nodes with high degree, is proposed for anti-community detection. LEOA consists of three stages: structural center detection, local anti-community expansion and group membership adjustment. Based on the formulation of DBM, we develop a synthetic benchmark DBM-Net for evaluating comparison algorithms in detecting known anti-community structures. Experiments on DBM-Net with up to 100000 nodes and 17 real-world networks demonstrate the effectiveness and efficiency of LEOA for anti-community detection in networks.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Models, Theoretical*

Grants and funding

This research was supported in part by the National Science and Technology Major Project of the Ministry of Science and Technology of China under grant 2018ZX10715003-002 (WY), the National Key Research and Development Program of China under grant 2017YFC1703900 (SY), the Sichuan Science and Technology Program under grant 2018PTDJ0084 (YL), and the US National Science Foundation (NSF) under grant 1652107 (XW). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.