Signed Social Network Vulnerability Analysis in Terms of Clustering coefficient and Balance Theory

Document Type : Original Article

Authors

1 Electrical and Computer Engineering Group, Golpayegan College of Engineering, Isfahan University of Technology, Golpayegan, Iran,

2 Department of Computer Engineering, Abadan Branch, Islamic Azad University, Abadan, Iran

Abstract

The robustness of a social network in response to unexpected events is still challenging for real-world networks. This paper addresses the challenge of evaluating social network robustness against unexpected events, particularly in real-world signed networks. We analyze the clustering vulnerability and balance of Signed Social Networks (SSNs) under the failure of important nodes. The main objective is to identify the critical nodes whose removal disrupts the network by weakening its clustering. It is evaluated by the Average Local Clustering Coefficient guided by the balance degree of the networks. To identify critical nodes, we propose parameter-based greedy strategies that remove nodes based on specific criteria. We conduct a comprehensive analysis of the real and synthetic SSNs generated by different well-known models and also online datasets. Our experiments demonstrate that removing a small percentage of nodes with the highest "Fans Minus Freaks (FMF)" value significantly reduces the network's clustering coefficient. Interestingly, centrality and PageRank metrics also play a role, but to a lesser extent, ranking second and third in terms of critical impact, respectively.

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TABRIZ JOURNAL OF ELECTRICAL ENGINEERING

Articles in Press, Corrected Proof
Available Online from 01 August 2024

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