Overlapping community detection in complex networks using symmetric binary matrix factorization

Zhang, Zhongyuan; Wang, Yong; Ahn, Yong‐Yeol

doi:10.1103/physreve.87.062803

Cited by 84 publications

(43 citation statements)

References 32 publications

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“…The proposed algorithm (NRATIO) is also compared with SBMF [20] and Bayesian NMF [13] which are closely related 18 to the proposed algorithm. Initially, the number of realizations was set to 100.…”

mentioning

confidence: 99%

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Overlapping community detection using neighborhood ratio matrix

Eustace

Wang

Cui

2015

Physica A: Statistical Mechanics and its Applications

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mentioning

confidence: 99%

“…In this regard, as in Ref. [20], 20 we set the number of realization to 10. Therefore each GNMI/NMI score is the average of 10 realizations.…”

mentioning

confidence: 99%

Overlapping community detection using neighborhood ratio matrix

Eustace

Wang

Cui

2015

Physica A: Statistical Mechanics and its Applications

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“…For example, in complex network with a clear community structure, vital vertices with higher betweenness centrality, which are called the overlapping vertices, belong to more than one com-munity commonly. To identify the overlapping vertices effectively benefits the community detection [5][6][7][8]. Meanwhile, the influential vertices can be quantified by various indexes.…”

Section: Introductionmentioning

confidence: 99%

A dynamical approach to identify vertices' centrality in complex networks

et al. 2017

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In this paper, we proposed a dynamical approach to assess vertices' centrality according to the synchronization process of the Kuramoto model. In our approach, the vertices' dynamical centrality is calculated based on the Difference of vertices' Synchronization Abilities (DSA), which are different from traditional centrality measurements that are related to the topological properties. Through applying our approach to complex networks with a clear community structure, we have calculated all vertices' dynamical centrality and found that vertices at the end of weak links have higher dynamical centrality. Meanwhile, we analyzed the robustness and efficiency of our dynamical approach through testing the probabilities that some known vital vertices were recognized. Finally, we applied our dynamical approach to identify community due to its satisfactory performance in assessing overlapping vertices. Our present work provides a new perspective and tools to understand the crucial role of heterogeneity in revealing the interplay between the dynamics and structure of complex networks.

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“…Conventional community detection techniques such as the edge betweenness method [1] and the modularity method [8] presume that each node in a graph is affiliated with only one community of a network. However, in reality an individual node can have multiple memberships resulting in overlapping community structure [9]- [11].…”

Section: Introductionmentioning

confidence: 99%

Local Fiedler vector centrality for detection of deep and overlapping communities in networks

Chen

Hero

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Abstract-In this paper, a new centrality called local Fiedler vector centrality (LFVC) is proposed to analyze the connectivity structure of a graph. It is associated with the sensitivity of algebraic connectivity to node or edge removals and features distributed computations via the associated graph Laplacian matrix. We prove that LFVC can be related to a monotonic submodular set function that guarantees that greedy node or edge removals come within a factor 1 − 1/e of the optimal non-greedy batch removal strategy. Due to the close relationship between graph topology and community structure, we use LFVC to detect deep and overlapping communities on real-world social network datasets. The results offer new insights on community detection by discovering new significant communities and key members in the network. Notably, LFVC is also shown to significantly outperform other well-known centralities for community detection.

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Overlapping community detection in complex networks using symmetric binary matrix factorization

Cited by 84 publications

References 32 publications

Overlapping community detection using neighborhood ratio matrix

Overlapping community detection using neighborhood ratio matrix

A dynamical approach to identify vertices' centrality in complex networks

Local Fiedler vector centrality for detection of deep and overlapping communities in networks

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