Fast algorithm for detecting community structure in networks
Abstract
Many networks display community structure—groups of vertices within which connections are dense but between which they are sparser—and sensitive computer algorithms have in recent years been developed for detecting this structure. These algorithms, however, are computationally demanding, which limits their application to small networks. Here we describe an algorithm which gives excellent results when tested on both computer-generated and real-world networks and is much faster, typically thousands of times faster, than previous algorithms. We give several example applications, including one to a collaboration network of more than 50 000 physicists.
- Publication:
-
Physical Review E
- Pub Date:
- June 2004
- DOI:
- arXiv:
- arXiv:cond-mat/0309508
- Bibcode:
- 2004PhRvE..69f6133N
- Keywords:
-
- 89.75.Hc;
- 05.10.-a;
- 87.23.Ge;
- 89.20.Hh;
- Networks and genealogical trees;
- Computational methods in statistical physics and nonlinear dynamics;
- Dynamics of social systems;
- World Wide Web Internet;
- Condensed Matter - Statistical Mechanics;
- Condensed Matter - Disordered Systems and Neural Networks
- E-Print:
- 5 pages, 4 figures