Attack induced cascading breakdown in complex networks

Liang Zhao,Ying-Cheng Lai,Kwangho Park,Thiago Henrique Cupertino

doi:10.1007/bf03192546

Abstract

Abstract The possibility that a complex network can be brought down by attack on a single or very few nodes through the process of cascading failures is of significant concern. In this paper, we investigate cascading failures in complex networks and uncover a phase-transition phenomenon in terms of the key parameter characterizing the node capacity. For parameter value below the phase-transition point, cascading failures can cause the network to disintegrate almost entirely. Then we show how to design networks of finite capacity that are safe against cascading breakdown. Our theory yields estimates for the maximally achievable network integrity via controlled removal of a small set of low-degree nodes.

Highlights

Complex networks arise in natural systems and they are an essential part of modern society
Many real complex networks, such as the World Wide Web (WWW), the Internet, and some electrical power grids, were found to be heterogeneous with power-law degree distribution [1, 2, 3, 4, 5, 6, 7] which means that the probability for a subset of nodes to possess a large number of links is not exponentially small, in contrast to random networks
We investigated cascading failures triggered by attacks on a single or a few nodes in scale-free networks and focused on the fundamental and practically important question of whether such failures can lead to disintegration of the network

Summary

INTRODUCTION

Complex networks arise in natural systems and they are an essential part of modern society. An intuitive reasoning based on the load distribution would suggest that, for a scale-free network, the possibility of breakdown triggered by attack on oevf eenveonnolynlya asisningglelennooddee ccaannnnoott be ignored. Imagine such a network that transports some physical quantities, or load. If the failing node carries a large amount of load, the consequence could be serious because this amount of load needs to be redistributed and it is possible that for some nodes, the new load exceeds their capacities. We study cascading failures in complex networks with focus on scale-free networks by using the ML model.

COMPLEX NETWORK MODELS

CASCADING FAILURE IN THE ML

Findings

CONCLUSIONS