Local modularity for community detection in complex networks

Ju, Xu; Hu, Tao; Zhang, Yan; Hu, Ke; Li, Jianming; Xu, Xiao-Ke; Liu, Cuicui; Chen, Shi

doi:10.1016/j.physa.2015.09.093

Cited by 58 publications

(27 citation statements)

References 27 publications

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“…Benefited from the underlying implications, many community-detection methods have been proposed and developed during the past decade [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]. Typical examples include the spectral analysis [22], random walk [23][24][25], label propagation [30], dynamic evolutionary [26][27][28][29], and modularity optimization [31,32].…”

Section: Introductionmentioning

confidence: 99%

Significance-based multi-scale method for network community detection and its application in disease-gene prediction

et al. 2020

PLoS ONE

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Community detection in complex networks is an important issue in network science. Several statistical measures have been proposed and widely applied to detecting the communities in various complex networks. However, due to the lack of flexibility resolution, some of them have to encounter the resolution limit and thus are not compatible with multi-scale structures of complex networks. In this paper, we investigated a statistical measure of interest for community detection, Significance [Sci. Rep. 3 (2013) 2930], and analyzed its critical behaviors based on the theoretical derivation of critical number of communities and the phase diagram in community-partition transition. It was revealed that Significance exhibits far higher resolution than the traditional Modularity when the intra-and inter-link densities of communities are obviously different. Following the critical analysis, we developed a multi-resolution version of Significance for identifying communities in the multi-scale networks. Experimental tests in several typical networks have been performed and confirmed that the generalized Significance can be competent for the multi-scale communities detection. Moreover, it can effectively relax the first-and second-type resolution limits. Finally, we displayed an important potential application of the multi-scale Significance in computational biology: diseasegene identification, showing that extracting information from the perspective of multi-scale module mining is helpful for disease gene prediction.

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Section: Introductionmentioning

confidence: 99%

Significance-based multi-scale method for network community detection and its application in disease-gene prediction

et al. 2020

PLoS ONE

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“…Modularity functions that take into account this information may provide a view of different depth into the community structures in the networks. Generally, this type of modularity functions can therefore be called as local modularity [ 47 , 48 ]. Differently from the global modularity, different communities are generally assigned different backgrounds in local modularity.…”

Section: Introductionmentioning

confidence: 99%

Global vs local modularity for network community detection

et al. 2018

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Community structures are ubiquitous in various complex networks, implying that the networks commonly be composed of groups of nodes with more internal links and less external links. As an important topic in network theory, community detection is of importance for understanding the structure and function of the networks. Optimizing statistical measures for community structures is one of most popular strategies for community detection in complex networks. In the paper, by using a type of self-loop rescaling strategy, we introduced a set of global modularity functions and a set of local modularity functions for community detection in networks, which are optimized by a kind of the self-consistent method. We carefully compared and analyzed the behaviors of the modularity-based methods in community detection, and confirmed the superiority of the local modularity for detecting community structures on large-size and heterogeneous networks. The local modularity can more quickly eliminate the first-type limit of modularity, and can eliminate or alleviate the second-type limit of modularity in networks, because of the use of the local information in networks. Moreover, we tested the methods in real networks. Finally, we expect the research can provide useful insight into the problem of community detection in complex networks.

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“…1. The network contains 66 nodes, 620 links, the average degree is 1.3338, the average out-closeness is 5.7023, the average in-closeness is 4.6657, global clustering coefficient is 0.793 [5][6][7][8][9].…”

Section: Construction Of Courses Relationship Network Model Of Electrmentioning

confidence: 99%

Curriculum Evaluation of Internet of Vehicles Converted from Electronic Information Engineering

Zou¹,

Liu²

2017

Advances in Computer Science Research

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Abstract.Internet of vehicles is a new professional, involving real-time access to vehicle data, car road and shore information exchange, traffic data analysis and processing of key technologies and public transport information services, big data and cloud computing. In this paper, we analyze the curriculum structure of internet of vehicles which transition from the electronic information engineering based on complex network theory. We construct the complex network models of electronic information engineering and internet of vehicles. And then we analyze the topology structure of network after replace the main course. The results show that the proposed network model can accurately assess the impact of curriculum changes on the curriculum system.

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Local modularity for community detection in complex networks

Cited by 58 publications

References 27 publications

Significance-based multi-scale method for network community detection and its application in disease-gene prediction

Significance-based multi-scale method for network community detection and its application in disease-gene prediction

Global vs local modularity for network community detection

Curriculum Evaluation of Internet of Vehicles Converted from Electronic Information Engineering

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