Community structure in social and biological networks.

@article{citeulike:81501, 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.}, address = {Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA. girvan@santafe.edu}, author = {Girvan, M. and Newman, M. E.}, citeulike-article-id = {81501}, doi = {10.1073/pnas.122653799}, issn = {0027-8424}, journal = {Proc Natl Acad Sci U S A}, keywords = {genetics, networks}, month = {June}, number = {12}, pages = {7821--7826}, posted-at = {2008-10-13 01:44:07}, priority = {2}, title = {Community structure in social and biological networks.}, url = {http://dx.doi.org/10.1073/pnas.122653799}, volume = {99}, year = {2002} }

TY - JOUR ID - citeulike:81501 L3 - citeulike-article-id:81501 N2 - 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. IS - 12 JF - Proc Natl Acad Sci U S A SN - 0027-8424 AD - Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA. girvan@santafe.edu EP - 7826 TI - Community structure in social and biological networks. VL - 99 SP - 7821 KW - genetics KW - networks AU - Girvan, M AU - Newman, ME PY - 2002/06/11/ UR - http://dx.doi.org/10.1073/pnas.122653799 ER -