Effect of Weight Thresholding on the Robustness of Real-World Complex Networks to Central Node Attacks

Jisha Mariyam John (), Michele Bellingeri, Divya Sindhu Lekha, Davide Cassi and Roberto Alfieri
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Jisha Mariyam John: Indian Institute of Information Technology, Kottayam 686635, India
Michele Bellingeri: Dipartimento di Scienze Matematiche, Fisiche e Informatiche, Università di Parma, 43124 Parma, Italy
Divya Sindhu Lekha: Indian Institute of Information Technology, Kottayam 686635, India
Davide Cassi: Dipartimento di Scienze Matematiche, Fisiche e Informatiche, Università di Parma, 43124 Parma, Italy
Roberto Alfieri: Dipartimento di Scienze Matematiche, Fisiche e Informatiche, Università di Parma, 43124 Parma, Italy

Mathematics, 2023, vol. 11, issue 16, 1-12

Abstract: In this study, we investigate the effect of weight thresholding (WT) on the robustness of real-world complex networks. Here, we assess the robustness of networks after WT against various node attack strategies. We perform WT by removing a fixed fraction of weak links. The size of the largest connected component indicates the network’s robustness. We find that real-world networks subjected to WT hold a robust connectivity structure to node attack even for higher WT values. In addition, we analyze the change in the top 30% of central nodes with WT and find a positive correlation in the ranking of central nodes for weighted node centralities. Differently, binary node centralities show a lower correlation when networks are subjected to WT. This result indicates that weighted node centralities are more stable indicators of node importance in real-world networks subjected to link sparsification.

Keywords: complex network; robustness; weight thresholding; node attack strategies; weak link removal (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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