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

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 connect...

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Bibliographic Details
Main Authors: Jisha Mariyam John, Michele Bellingeri, Divya Sindhu Lekha, Davide Cassi, Roberto Alfieri
Format: Article
Language:English
Published: MDPI AG 2023-08-01
Series:Mathematics
Subjects:
Online Access:https://www.mdpi.com/2227-7390/11/16/3482
Description
Summary: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.
ISSN:2227-7390