Adversarial Attacks on Clustering Coefficient in Complex Networks

The clustering coefficient is a crucial characteristic of networks, providing insights into their local compactness. However, concerns arise regarding the clustering coefficient’s robustness when networks are subjected to adversarial attacks. In this brief, we introduce and formalize the concept of clustering coefficient attack and propose an efficient strategy to attack it by rewiring a small number of links. Our approach leverages Simulated Annealing (SA) to create a clustering coefficient attack method. We conduct extensive experiments on five real-world networks to validate the vulnerability of the clustering coefficient under adversarial attacks. Surprisingly, we find that disturbing as little as 1% of the edges can lead to an increase of over 50% in the clustering coefficient. These results underscore the pressing need to explore ways to enhance the clustering coefficient’s robustness against such attacks in future research. Addressing this challenge could be an intriguing avenue for further investigation.