Detecting Communities in Massive Networks Efficiently with Flexible Resolution

  • Qi YeEmail author
  • Bin Wu
  • Bai Wang
Part of the Lecture Notes in Social Networks book series (LNSN, volume 6)


Currently, community detection has led to a huge interest in data analysis on real-world networks. However, the high computationally demanding of most community detection algorithms limits their applications. In this chapter, we propose an iterative heuristic algorithm (called MMO algorithm) to extract the community structure in large networks based on local multi-resolution modularity optimization whose time complexity is near linear and space complexity is linear. The effectiveness of MMO algorithm is demonstrated by extensive experiments on lots of computer generated graphs and publically available real-world graphs. We also extend MMO algorithm to extract communities in distributed environment and use it to explore a massive call graph on a normal PC. The results show that MMO algorithm is very efficient, and it may enhance our ability to explore massive networks in real time.


Jaccard Index Call Graph Community Detection Algorithm Modularity Optimization Patent Citation Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank M. E. J. Newman, Alex Arenas and Jure Leskovec for providing us the network data sets. This work is supported by the National Science Foundation of China (No. 90924029, 60905025, 61074128). It is also supported the National Hightech R&D Program of China (No.2009AA04Z136).


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Copyright information

© Springer-Verlag Wien 2013

Authors and Affiliations

  1. 1.Beijing University of Posts and TelecommunicationsBeijingChina

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