Emergence of local structures in complex network:common neighborhood drives the network evolution

Cui, Aixiang; Fu, Yan; Shang, Mingsheng; Chen, Duanbing; Zhou, Tao

doi:10.7498/aps.60.038901

Cited by 12 publications

(3 citation statements)

References 33 publications

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“…There is a modular structure in the network model proposed, modularity measure is proposed to describe the strength of module structure in a network. According to the Newman and Girvan's study [10] the network modular measure is expressed as…”

Section: Modularity Measurementioning

confidence: 99%

An evolving network model with modular growth

Zou¹,

Liu²,

Lei³

et al. 2012

Chinese Phys. B

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In this paper, we propose an evolving network model growing fast in units of module, according to the analysis of the evolution characteristics in real complex networks. Each module is a small-world network containing several interconnected nodes and the nodes between the modules are linked by preferential attachment on degree of nodes. We study the modularity measure of the proposed model, which can be adjusted by changing the ratio of the number of innermodule edges and the number of inter-module edges. In view of the mean-field theory, we develop an analytical function of the degree distribution, which is verified by a numerical example and indicates that the degree distribution shows characteristics of the small-world network and the scale-free network distinctly at different segments. The clustering coefficient and the average path length of the network are simulated numerically, indicating that the network shows the small-world property and is affected little by the randomness of the new module.

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Section: Modularity Measurementioning

confidence: 99%

An evolving network model with modular growth

Zou¹,

Liu²,

Lei³

et al. 2012

Chinese Phys. B

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“…Therefore, betweenness provides a method of identifying intercommunity edges. [20,22] Figure 1 shows a graph with edge-betweenness labeled beside the edge.…”

Section: Betweenness and Community Detectionmentioning

confidence: 99%

Betweenness-based algorithm for a partition scale-free graph

Zhang

Tang

et al. 2011

Chinese Phys. B

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“…Lü et al [19] suggested that in a network with large clustering coefficient, CN can provide competitively accurate predictions compared with the indices making use of global information. Very recently, Cui et al [20] revealed that the nodes with more common neighbors are more likely to form new links in a growing network. Simply counting the number of common neighbors indicates that each common neighbor gives equal contribution to the connection likelihood.…”

mentioning

confidence: 99%

Link prediction in complex networks: A local naïve Bayes model

Liu

Zhang

Lü

et al. 2011

EPL

206

140

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Common-neighbor-based method is simple yet effective to predict missing links, which assume that two nodes are more likely to be connected if they have more common neighbors. In such method, each common neighbor of two nodes contributes equally to the connection likelihood. In this Letter, we argue that different common neighbors may play different roles and thus lead to different contributions, and propose a local naïve Bayes model accordingly. Extensive experiments were carried out on eight real networks. Compared with the common-neighbor-based methods, the present method can provide more accurate predictions. Finally, we gave a detailed case study on the US air transportation network.

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Emergence of local structures in complex network:common neighborhood drives the network evolution

Cited by 12 publications

References 33 publications

An evolving network model with modular growth

An evolving network model with modular growth

Betweenness-based algorithm for a partition scale-free graph

Link prediction in complex networks: A local naïve Bayes model

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