The attack tolerance of community structure in complex networks

TL;DR

This paper investigates how different types of perturbations affect the robustness of community structures in complex networks, revealing that targeted attacks cause more damage than random disturbances.

Contribution

It introduces two novel perturbation methods based on clustering coefficient and attacking triangles to study community structure robustness.

Findings

Targeted attacks significantly disrupt community structures.

Community structures become less clear after perturbations.

Targeted attacks are more damaging than random disturbances.

Abstract

Robustness is an important property of complex networks. Up to now, there are plentiful researches focusing on the network's robustness containing error and attack tolerance of network's connectivity and the shortest path. In this paper, the error and attack tolerance of network's community structure are studies through randomly and purposely disturbing interaction of networks. Two purposely perturbation methods are designed, that one methods is based on cluster coefficient and the other is attacking triangle. Dissimilarity function D is used to quantify the changes of community structure and modularity Q is used to quantify the significance of community structure. The numerical results show that after perturbation, network's community structure is damaged to be more unclear. It is also discovered that purposely attacking damages more to the community structure than randomly attacking.