Community structure in social and biological networks.

Full Abstract

A number of recent studies have focused on the statistical properties of networked systems such as social networks and the Worldwide Web. Researchers have concentrated particularly on a few properties that seem to be common to many networks:
the small-world property, power-law degree distributions, and network transitivity. In this article, we highlight another property that is found in many networks, the property of community structure, in which network nodes are joined together in tightly knit groups, between which there are only looser connections. We propose a method for detecting such communities, built around the idea of using centrality indices to find community boundaries. We test our method on computer-generated and real-world graphs whose community structure is already known and find that the method detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well known--a collaboration network and a food web--and find that it detects significant and informative community divisions in both cases.

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Author information

Author/s: Girvan, M (M); Newman, M E J (ME);

Affiliation: Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA. girvan(-atsign-)santafe.edu

Journal and publication information

Publication Type: Journal Article; Research Support, U.S. Gov't, Non-P.H.S.; Review

Journal: Proceedings of the National Academy of Sciences of the United States of America (Proc Natl Acad Sci U S A), published in United States. (Language: eng)

Reference: 2002-Jun; vol 99 (issue 12) : pp 7821-6

Dates: Created 2002/06/12; Completed 2002/08/02; Revised 2006/11/15;

PMID: 12060727, status: MEDLINE (last retrieval date: 11/6/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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