Letter

Nature 435, 814-818 (9 June 2005) | doi:10.1038/nature03607; Received 17 January 2005; Accepted 7 April 2005

Uncovering the overlapping community structure of complex networks in nature and society

Gergely Palla1,2, Imre Derényi2, Illés Farkas1 & Tamás Vicsek1,2

  1. Biological Physics Research Group of the Hungarian Academy of Sciences, Pázmány P. stny. 1A, H-1117 Budapest, Hungary
  2. Department of Biological Physics, Eötvös University, Pázmány P. stny. 1A, H-1117 Budapest, Hungary

Correspondence to: Tamás Vicsek1,2 Correspondence and requests for materials should be addressed to T.V. (Email: vicsek@angel.elte.hu).

Many complex systems in nature and society can be described in terms of networks capturing the intricate web of connections among the units they are made of1, 2, 3, 4. A key question is how to interpret the global organization of such networks as the coexistence of their structural subunits (communities) associated with more highly interconnected parts. Identifying these a priori unknown building blocks (such as functionally related proteins5, 6, industrial sectors7 and groups of people8, 9) is crucial to the understanding of the structural and functional properties of networks. The existing deterministic methods used for large networks find separated communities, whereas most of the actual networks are made of highly overlapping cohesive groups of nodes. Here we introduce an approach to analysing the main statistical features of the interwoven sets of overlapping communities that makes a step towards uncovering the modular structure of complex systems. After defining a set of new characteristic quantities for the statistics of communities, we apply an efficient technique for exploring overlapping communities on a large scale. We find that overlaps are significant, and the distributions we introduce reveal universal features of networks. Our studies of collaboration, word-association and protein interaction graphs show that the web of communities has non-trivial correlations and specific scaling properties.

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