Detecting Communities in Massive Networks Efficiently with Flexible Resolution
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.
KeywordsJaccard Index Call Graph Community Detection Algorithm Modularity Optimization Patent Citation Network
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).
- 2.Blondel, V.D., Guillaume, J., et al.: Fast unfolding of communities in large networks. J. Stat. Mech. 10008, 1-Ű12 (2008)Google Scholar
- 6.Dongen, S.V.: Graph clustering by flow simulation. Ph.D. thesis, University of Utrecht (2000)Google Scholar
- 20.Tomita, E., Tanaka, A., Takahashi, H.: The worst-case time complexity for generating all maximal cliques. In: COCOON, pp. 161–170. Springer, Berlin (2004)Google Scholar
- 21.Ye, Q., Wu, B., et al: TeleComVis: exploring temporal communities in telecom networks. In: ECML PKDD, pp. 755–758. Springer, Heidelberg (2009)Google Scholar
- 22.Ye, Q., Wu, B., et al: Detecting communities in massive networks based on local community attractive force optimization. In: International Conference on Advances in Social Network Analysis and Mining, pp. 291–295. IEEE Computer Society, Los Alamitos (2010)Google Scholar