Online community detection for large complex networks

PLoS One. 2014 Jul 25;9(7):e102799. doi: 10.1371/journal.pone.0102799. eCollection 2014.

Abstract

Complex networks describe a wide range of systems in nature and society. To understand complex networks, it is crucial to investigate their community structure. In this paper, we develop an online community detection algorithm with linear time complexity for large complex networks. Our algorithm processes a network edge by edge in the order that the network is fed to the algorithm. If a new edge is added, it just updates the existing community structure in constant time, and does not need to re-compute the whole network. Therefore, it can efficiently process large networks in real time. Our algorithm optimizes expected modularity instead of modularity at each step to avoid poor performance. The experiments are carried out using 11 public data sets, and are measured by two criteria, modularity and NMI (Normalized Mutual Information). The results show that our algorithm's running time is less than the commonly used Louvain algorithm while it gives competitive performance.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Community Networks*
  • Humans
  • Internet
  • Models, Theoretical*
  • Social Behavior*

Grant support

This work has been supported by National Key Basic Research Program of China(2013CB329504) <http://www.973.gov.cn/English/Index.aspx> and Qianjiang Talent Program of Zhejiang(2011R10078)<http://www.zjhrss.gov.cn/col/col1128/index.html>. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.