Zheng Guo’s research while affiliated with Qingdao University of Technology and other places

Complex networks are often subjected to failure and attack. Recent work has addressed the resilience of complex networks to either random or intentional deletion of nodes or links. Here we simulate the breakdown of the small-world network and the scale-free network under node failure or attacks. We analyze and discuss the dynamic correlation between degree and betweenness in the process of attack. The simulation results show that the correlation for scale-free network obeys a power law distribution until the network collapses, while it represents irregularly for small-world network.

The problem of finding the best strategy to attack network or immunize population with a minimal number of nodes attracts current research interest. The assessment of node importance has been a fundamental issue in such research of complex networks. In this paper, we propose a new concept called mapping entropy (ME) to identify the importance of a node in the complex network. The concept is established according to the local information which considers the correlation among all neighbours of a node. We evaluate the efficiency of the centrality by static attacks and dynamic attacks on standard network models and real-world networks. The simulation result shows that the new centrality is more efficient than traditional attack strategies, no matter in static manner or dynamic manner.

The invulnerability of complex networks is an important issue in that the behavior of scale-free network differs from that of exponential network. According to the structural characteristics of the networks, we propose two new attack strategies named IDB (initial degree and betweenness) and RDB (recalculated degree and betweenness). The strategies are originated from ID (initial degree distribution) and RD (recalculated degree distribution) strategies in which attacks are based on initial structural information of a network. The probability of node removals depends on a new metric combining degree centrality and betweenness centrality. We evaluate the efficiency of the proposed strategies on one real-world network and three network models. Experimental results indicate that the proposed strategies are more efficient than the traditional ID and RD strategies. Specially, the WS small-world network behaves more sensitive to the proposed strategies. The attack efficiency of RDB strategy is improved by 20% to RD strategy, and IDB strategy is improved by 40% to ID strategy.