Extracting Communities from Complex Networks by the k-dense Method

Saito, Kazumi; Yamada, Tatsuro; Kazama, Kazuhiro

doi:10.1109/icdmw.2006.76

Cited by 34 publications

(64 citation statements)

References 6 publications

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“…This can be achieved with the m -core, defined as the maximal subgraph such that all its edges have, at least, multiplicity m within it. This concept was developed in3132 under the name of k -dense decomposition. The edges in a k -dense graph have multiplicity m = k − 2.…”

Section: Resultsmentioning

confidence: 99%

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Deciphering the global organization of clustering in real complex networks

Colomer-de-Simón

Serrano

Beiró

et al. 2013

Sci Rep

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We uncover the global organization of clustering in real complex networks. To this end, we ask whether triangles in real networks organize as in maximally random graphs with given degree and clustering distributions, or as in maximally ordered graph models where triangles are forced into modules. The answer comes by way of exploring m-core landscapes, where the m-core is defined, akin to the k-core, as the maximal subgraph with edges participating in at least m triangles. This property defines a set of nested subgraphs that, contrarily to k-cores, is able to distinguish between hierarchical and modular architectures. We find that the clustering organization in real networks is neither completely random nor ordered although, surprisingly, it is more random than modular. This supports the idea that the structure of real networks may in fact be the outcome of self-organized processes based on local optimization rules, in contrast to global optimization principles.

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Section: Resultsmentioning

confidence: 99%

“…To compute m -cores efficiently, we develop a new approach, different from the one in3132. We first map the original graph G into a hypergraph G *, where edges in G become vertices in G * and where each triangle in the original graph is mapped into an edge (a 3-tuple) in G *.…”

Section: Methodsmentioning

confidence: 99%

Deciphering the global organization of clustering in real complex networks

Colomer-de-Simón

Serrano

Beiró

et al. 2013

Sci Rep

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“…The proposed protein complex models such as -clique community [20], Hyper-clique [22], -dense [28], fuzzy community [29], and Quasi-clique [30] are all based on dense subgraphs. However, the real protein complexes do not always have highly connected topologies.…”

Section: A Quantitative Definition Of Complexmentioning

confidence: 99%

Identifying Protein Complexes From Interactome Based on Essential Proteins and Local Fitness Method

Wang

Chen

Liu

et al. 2012

IEEE Trans.on Nanobioscience

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High-throughput experimental technologies, along with computational predictions, have promoted the emergence of large-scale interactome for numerous organisms. Identification of protein complexes from these interactome networks is crucial to understand principles of cellular organization and predict protein functions. Protein complexes are generally considered as dense subgraphs. However, the real protein complexes do not always have highly connected topologies. In this paper, a novel protein complex identifying method, named EPOF, is proposed, using essential proteins and the local metric of vertex fitness. In EPOF, cliques in the subnetwork which is consisted by the essential proteins are firstly considered as seeds, which are ordered according to their size and the number of their neighbors. A protein complex is extended from a seed based on the evaluation of its neighbors' fitness value. Then, the similar procedure is applied to the cliques identified in the subnetwork which is consisted by the proteins which is not clustered in the first step. When EPOF identifies complexes by expanding essential protein cliques, the essential proteins have higher priority and lower threshold. When it identifies complexes by expanding nonessential protein cliques, the nonessential proteins have higher priority and lower threshold. Finally, the last step, we output the identified complexes set. The proposed algorithm EPOF is applied to the unweighted and weighted interaction networks of S. cerevisiae and detects many well known protein complexes. We compare the performances of EPOF to other ten previous algorithms, including EAGLE, NFC, MCODE, DPClus, IPCA, CPM, MCL, CMC, SPICi, and Core-Attachment. Experimental results show that EPOF outperforms other previous competing algorithms in terms of matching with known complexes, sensitivity, specificity, f-measure, function enrichment and accuracy. The program and related files available on https://github.com/gangchen/epof.

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“…To calculate such a score, we use the centrality score of G since a generic tag has a lot of edges to similar tags and thus has a high centrality score. We adopted k-dense [3] for determining the centrality score.…”

Section: Algorithmmentioning

confidence: 99%

Topigraphy

Fujimura

Matsubayashi

et al. 2008

Proceedings of the 17th International Conference on World Wide Web

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This paper proposes a new method for displaying large-scale tag clouds. We use a topographical image that helps users to grasp the relationship among tags intuitively as a background to the tag clouds. We apply this interface to a blog navigation system and show that the proposed method enables users to find the desired tags easily even if the tag clouds are very large, 5,000 and above tags. Our approach is also effective for understanding the overall structure of a large amount of tagged documents.

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Extracting Communities from Complex Networks by the k-dense Method

Cited by 34 publications

References 6 publications

Deciphering the global organization of clustering in real complex networks

Deciphering the global organization of clustering in real complex networks

Identifying Protein Complexes From Interactome Based on Essential Proteins and Local Fitness Method

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