publication . Preprint . Article . Other literature type . 2002

Community structure in social and biological networks

Girvan, M.; Newman, M. E. J.;
Open Access English
  • Published: 11 Jun 2002
Abstract
A number of recent studies have focused on the statistical properties of networked systems such as social networks and the World-Wide Web. Researchers have concentrated particularly on a few properties which seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this paper, we highlight another property which 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 new method for detecting such communities, built around the idea of using centrality indices to fin...
Subjects
free text keywords: Condensed Matter - Statistical Mechanics, Condensed Matter - Disordered Systems and Neural Networks, Physical Sciences, Network science, Data mining, computer.software_genre, computer, Social network, business.industry, business, Computer science, Data science, Clique percolation method, Assortative mixing, Network motif, Modularity (networks), Scientific collaboration network, Biological network
Related Organizations
Funded by
NSF| Structure and Dynamics of Social Networks and Other Networked Systems
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 0109086
  • Funding stream: Directorate for Mathematical & Physical Sciences | Division of Mathematical Sciences
28 references, page 1 of 2

[1] S. H. Strogatz, Exploring complex networks. Nature 410, 268-276 (2001). [OpenAIRE]

[2] S. Wasserman and K. Faust, Social Network Analysis. Cambridge University Press, Cambridge (1994).

[3] J. Scott, Social Network Analysis: A Handbook. Sage Publications, London, 2nd edition (2000).

[4] D. J. Watts and S. H. Strogatz, Collective dynamics of 'small-world' networks. Nature 393, 440-442 (1998).

[5] L. A. N. Amaral, A. Scala, M. Barth´el´emy, and H. E. Stanley, Classes of small-world networks. Proc. Natl. Acad. Sci. USA 97, 11149-11152 (2000).

[6] M. E. J. Newman, The structure of scientific collaboration networks. Proc. Natl. Acad. Sci. USA 98, 404-409 (2001).

[7] M. Faloutsos, P. Faloutsos, and C. Faloutsos, On powerlaw relationships of the internet topology. Computer Communications Review 29, 251-262 (1999). [OpenAIRE]

[8] R. Albert, H. Jeong, and A.-L. Barab´asi, Diameter of the world-wide web. Nature 401, 130-131 (1999).

[9] A. Broder, R. Kumar, F. Maghoul, P. Raghavan, S. Rajagopalan, R. Stata, A. Tomkins, and J. Wiener, Graph structure in the web. Computer Networks 33, 309-320 (2000).

[10] R. J. Williams and N. D. Martinez, Simple rules yield complex food webs. Nature 404, 180-183 (2000).

[11] H. Jeong, B. Tombor, R. Albert, Z. N. Oltvai, and A.- L. Barab´asi, The large-scale organization of metabolic networks. Nature 407, 651-654 (2000).

[12] D. A. Fell and A. Wagner, The small world of metabolism. Nature Biotechnology 18, 1121-1122 (2000).

[13] I. Pool and M. Kochen, Contacts and influence. Social Networks 1, 1-48 (1978).

[14] S. Milgram, The small world problem. Psychology Today 2, 60-67 (1967).

[15] A.-L. Barab´asi and R. Albert, Emergence of scaling in random networks. Science 286, 509-512 (1999).

28 references, page 1 of 2
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