[3] Univ London Imperial Coll Sci Technol & Med, Inst Math Sci, London SW7 2PG, England
来源:
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
|
2008年
关键词:
random graphs;
networks;
new applications of statistical mechanics;
D O I:
10.1088/1742-5468/2008/10/P10008
中图分类号:
O3 [力学];
学科分类号:
08 ;
0801 ;
摘要:
We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection methods in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity. This is shown first by identifying language communities in a Belgian mobile phone network of 2 million customers and by analysing a web graph of 118 million nodes and more than one billion links. The accuracy of our algorithm is also verified on ad hoc modular networks.