Evaluation of community vulnerability based on communicability and structural dissimilarity

The exploration of community features is a key issue in network science and data mining. As a vital structural characteristic, community vulnerability has been paid great deal of concern. Recent works underline that many internal and external parameters to quantify community vulnerability necessaril...

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Veröffentlicht in:Physica A 2022-11, Vol.606, p.128079, Article 128079
Hauptverfasser: Chen, Gaolin, Zhou, Shuming, Li, Min, Zhang, Hong
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Sprache:eng
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Zusammenfassung:The exploration of community features is a key issue in network science and data mining. As a vital structural characteristic, community vulnerability has been paid great deal of concern. Recent works underline that many internal and external parameters to quantify community vulnerability necessarily improve conformity with topology, but are suffering from a shortage of comprehensiveness. In this paper, we propose a novel metric, namely communication and structural heterogeneity method (CSH), designed to characterize topological information by communicability and structural dissimilarity. CSH is a global path-related strategy which is based on community dissimilarity. Furthermore, intra-link number, average communicability, topological heterogeneity in communities, as well as inter-link number and structural dissimilarity between communities are employed. Thus, a more detailed evaluation of community vulnerability is suggested. The effectiveness and accuracy of CSH are verified by empirical results in real-world networks. Moreover, the propagation dynamic SIR model and simulations of random and deliberate attack are utilized to validate rationality. Meanwhile, the correlation between node importance (vulnerability) and community vulnerability is explored through experiments. The proposed method (CSH) shows its superiority when comparing it to some state-of-the-art methods. •A novel metric to quantify community vulnerability based on communicability and structural dissimilarity is proposed.•The dissimilarities based on communicability inside and outside the community are utilized to distinguish community vulnerability.•Multiple factors, such as including intra-link number, average communicability, topological heterogeneity in communities, inter-link number and structural dissimilarity between communities, are combined.•The experiments based on SIR model and the simulation of random and deliberate node attack verify the effectiveness and accuracy of the proposed strategy.
ISSN:0378-4371
1873-2119
DOI:10.1016/j.physa.2022.128079