title: Improving the robustness of complex networks with preserving community structure.
abstract: Complex networks are everywhere, such as the power grid network, the airline network, the protein-protein interaction network, and the road network. The networks are 'robust yet fragile', which means that the networks are robust against random failures but fragile under malicious attacks. The cascading failures, system-wide disasters and intentional attacks on these networks are deserving of in-depth study. Researchers have proposed many solutions to improve the robustness of these networks. However whilst many solutions preserve the degree distribution of the networks, little attention is paid to the community structure of these networks. We argue that the community structure of a network is a defining characteristic of a network which identifies its functionality and thus should be preserved. In this paper, we discuss the relationship between robustness and the community structure. Then we propose a 3-step strategy to improve the robustness of a network, while retaining its community structure, and also its degree distribution. With extensive experimentation on representative real-world networks, we demonstrate that our method is effective and can greatly improve the robustness of networks, while preserving community structure and degree distribution. Finally, we give a description of a robust network, which is useful not only for improving robustness, but also for designing robust networks and integrating networks.
authors: School of Computer Science and Engineering, Beihang University, Beijing, China.
authors: Yang
authors: Y
authors: Yang
authors: School of Computer Science and Engineering, Beihang University, Beijing, China.
authors: Zhoujun
authors: Z
authors: Li
authors: School of Computer Science and Engineering, Beihang University, Beijing, China.
authors: Yan
authors: Y
authors: Chen
authors: School of Computer Science and Engineering, Beihang University, Beijing, China.
authors: Xiaoming
authors: X
authors: Zhang
authors: School of Computer Science and Engineering, Beihang University, Beijing, China.
authors: Senzhang
authors: S
authors: Wang
mesh_terms: D000465:Algorithms; D008962:Models, Theoretical