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MEBC: social network immunization via motif-based edge-betweenness centrality

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Abstract

Immunization of social networks has attracted increasing attention over the last decade. Various algorithms have been proposed based on the topological structure of networks, such as the degree and betweenness of nodes. However, most of these studies have only observed the basic topological structure at the level of individual nodes, ignoring higher-order structures captured by network motifs, which may lead to insufficient performance. Besides, immunization based on the connectivity pattern of nodes such as the degree in a social network may cause integrity problems and also interfere in other users’ regular activities because the absence of the hub nodes can greatly impair the connectivity of the network. Thus, we introduce the edge-betweenness as a metric of social network immunization that is much more effective than other traditional measures and reflects the significant role that edges play in reducing the damage and cost of the immunizing process. In this paper, a new network immunization algorithm is proposed by combining higher-order structures and edge-betweenness to select an edge set to be immunized. We conduct extensive experiments on real-world networks to show that the new algorithm can significantly improve the effectiveness of the immunization and reduce the impact of the structure of the network.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (No. 61976162, No. 82174230), Key Projects of Guangdong Natural Science Foundation (No. 2018B030311003), Science and Technologies Major Project of Hubei Province (Next-Generation AI Technologies) (No. 2019AEA170). Joint Fund for Translational Medicine and Interdisciplinary Research of Zhongnan Hospital of Wuhan University (No. ZNJC202016).

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Authors and Affiliations

  1. School of Computer Science, Wuhan University, Wuhan, China

    Kuang Gao & Wenbin Hu

  2. Shenzhen Research Institute, Wuhan University, Wuhan, China

    Wenbin Hu

  3. Department of Information Network, Wuhan Maritime Communications Research Institute, Wuhan, China

    Guocai Yuan, Yang Yang & Ying Fan

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  1. Kuang Gao
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  2. Guocai Yuan
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  3. Yang Yang
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  4. Ying Fan
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  5. Wenbin Hu
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Correspondence to Wenbin Hu.

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https://github.com/kgao-whu/MEBC.

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Gao, K., Yuan, G., Yang, Y. et al. MEBC: social network immunization via motif-based edge-betweenness centrality. Knowl Inf Syst 64, 1263–1281 (2022). https://doi.org/10.1007/s10115-022-01671-y

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