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The existing methods e networks are useful if the

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Unformatted text preview: etworks. Our studies of n and protein interaction graphs ies has non-trivial correlations and contain parts in which the nodes ted to each other than to the rest of nodes are usually called clusters, or modules8,10,11–13; they have no ition. In spite of this ambiguity, in networks is a signature of the systems5,14. The existing methods e networks are useful if the commube interpreted in terms of separated b and refs 10, 15, 16–18). However, terized by well-defined statistics of nities. This can be illustrated by the ch of us belongs to, including those overlapping Nature, 435 k-clique aggregation (Palla et al.) Figure 1 | Illustration of the concept of overlapping communities. a, The black• Build up k-cliques. either of the authors overlapping ith dot in the middle represents k-cliques with of this paper, w nodes several of his communities around. Zooming in on the scientific community communities demonstrates the nested and overlapping structure of the communities, and depicting the cascades of communities starting from some members exemplifies the interwoven structure of the network of communities. • What size k to choose? (Typically try 2, 3, 4, 5) b, Divisive and agglomerative methods grossly fail to identify the communities when overlaps are significant. c, An example of overlapping • communities at k assigned to communities k-cliqueNot all nodes ¼ 4. The yellow community overlaps the blue one in a single node, whereas it shares two nodes and a link with the green one. These overlapping regions are emphasized in red. Notice that any k-clique (complete subgraph of size k) can be reached only from the k-cliques of the are in overlapping Aggregating/collapsing: treat k-cliques as nodes and recurse H.-W. Shen, X. Cheng, K. Cai, and M.-B. Hu, Physica A 388, 1706 (2009). • Find maximal k-clique, collapse into one node • Repeat until all nodes now assigned to a clique Noise in community detection Haoran Wen, E. A. Leicht, and R. M. D’Souza, “Improving community detection in networks HAORAN WEN, E. A. LEICHT, AND RAISSA M. D’SOUZA by targeted node removal” Physical Review E 83, 016114, 2011. (a) (b) networks but has no networks [24–26]. Recently severa detecting communi multiple communi [11–16]. However, to one-quarter o...
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