The effect of size heterogeneity on community identification in complex networks

Leon Danon*, Albert Díaz-Guilera, Alex Arenas

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

179 Citations (Scopus)


Identifying community structure can be used as a potent tool in the analysis and understanding of the structure of complex networks. Up to now, methods for evaluating the performance of identification algorithms have used ad hoc networks with communities of equal size. We show that inhomogeneities in community sizes can and do affect the performance of algorithms considerably, and propose an alternative method which takes these factors into account. Furthermore, we propose a simple modification of the algorithm proposed by Newman for community detection (2004 Phys. Rev. E 69 066133) which treats communities of different sizes on an equal footing, and show that it outperforms the original algorithm while retaining its speed.

Original languageEnglish
Article numberP11010
JournalJournal of Statistical Mechanics: Theory and Experiment
Issue number11
Publication statusPublished - 1 Nov 2006


  • Analysis of algorithms
  • Network dynamics


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