Quantifying and identifying the overlapping community structure in networks

Shen, Huawei; Cheng, Xueqi; Guo, Jiafeng

doi:10.1088/1742-5468/2009/07/p07042

Cited by 109 publications

(91 citation statements)

References 33 publications

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“…Many methods have been proposed and applied successfully to some specific complex networks [6][7][8][9][10][11][12][13][14][15][16]. For review, the reader can refer to Ref.…”

Section: Introductionmentioning

confidence: 99%

Exploring the structural regularities in networks

2011

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In this paper, we consider the problem of exploring structural regularities of networks by dividing the nodes of a network into groups such that the members of each group have similar patterns of connections to other groups. Specifically, we propose a general statistical model to describe network structure. In this model, group is viewed as hidden or unobserved quantity and it is learned by fitting the observed network data using the expectation-maximization algorithm. Compared with existing models, the most prominent strength of our model is the high flexibility. This strength enables it to possess the advantages of existing models and overcomes their shortcomings in a unified way. As a result, not only broad types of structure can be detected without prior knowledge of what type of intrinsic regularities exist in the network, but also the type of identified structure can be directly learned from data. Moreover, by differentiating outgoing edges from incoming edges, our model can detect several types of structural regularities beyond competing models. Tests on a number of real world and artificial networks demonstrate that our model outperforms the state-of-the-art model at shedding light on the structural features of networks, including the overlapping community structure, multipartite structure and several other types of structure which are beyond the capability of existing models.

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“…Many methods have been proposed and applied successfully to some specific complex networks [6][7][8][9][10][11][12][13][14][15][16]. For review, the reader can refer to Ref.…”

Section: Introductionmentioning

confidence: 99%

Exploring the structural regularities in networks

2011

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“…A maximal clique is a clique that is not a subset of any other clique in a community network [36]. These maximal complete subgraphs are simply called cliques, and the difference between k-cliques and cliques is that k-cliques can be subsets of larger complete subcommunities.…”

Section: Definitionmentioning

confidence: 99%

Structural and functional analytics for community detection in large-scale complex networks

Chopade

Zhan

2015

Journal of Big Data

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Community structure is thought to be one of the main organizing principles in most complex networks. Big data and complex networks represent an area which researchers are analyzing worldwide. Of special interest are groups of vertices within which connections are dense. In this paper we begin with discussing community dynamics and exploring complex network structural parameters. We put forward structural and functional models for analyzing complex networks under situations of perturbations. We introduce modified adjacency and modified Laplacian matrices. We further introduce network or degree centrality (weighted Laplacian centrality) based on modified Laplacian, weighted micro-community centrality. We discuss its robustness and importance for micro-community detection for social and technological complex networks with overlapping communities. We also introduce 'k-clique sub-community' overlapping community detection based on degree and weighted micro-community centrality. The proposed algorithms use optimal partition of k-clique sub-community for modularity optimization. We establish relationship between degree centrality and modularity. This proposed method with modified adjacency matrix helps us solve NP-hard problem.

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“…Nicosia et al [38] extended the modularity to overlapping case, and then proposed a genetic algorithm to optimize their quality function. Shen et al [39] also presented an overlapping measure and then employed the Blondel's algorithm to optimize it. In addition, Zhang et al successively presented a fuzzy c-means method [40] and negative matrix factorization method [41] for finding a good overlapping division.…”

Section: A Evolutionary Methods For Community Detection B Overlappinmentioning

confidence: 99%

Identifying overlapping communities in networks using evolutionary method

Zhan

Guan

Chen

et al. 2016

Physica A: Statistical Mechanics and its Applications

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Abstract-Community structure is a typical property of many real-world networks, and has become a key to understand the dynamics of the networked systems. In these networks most nodes apparently lie in a community while there often exists a few nodes straddling several communities. An ideal algorithm for community detection is preferable which can identify the overlapping communities in such networks. To represent an overlapping division we develop a encoding schema composed of two segments, the first one represents a disjoint partition and the second one represents a extension of the partition that allows of multiple memberships. We give a measure for the informativeness of a node, and present an evolutionary method for detecting the overlapping communities in a network.

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Quantifying and identifying the overlapping community structure in networks

Cited by 109 publications

References 33 publications

Exploring the structural regularities in networks

Exploring the structural regularities in networks

Structural and functional analytics for community detection in large-scale complex networks

Identifying overlapping communities in networks using evolutionary method

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