Analyzing Robustness of Complex Networks Against Incomplete Information

Large scale networked systems are playing an indispensable role in modern society, and thus the robustness of these systems against random failures or malicious attacks has become a critical research issue. As a major threat to network robustness, cascading failures have attracted increasing research attention in the past decades. Previous studies have put forward many heuristic methods to investigate the network robustness against cascading failure problems. However, most of them assume that the attackers can obtain the complete topology information of the network, which may not be available in practice. To tackle this problem, we use the link prediction methods to restore the missing information (i.e., the topological structure of networks) of the network first, and then utilize the predicted information to further help distinguish the critical nodes in the network systems from the attacker’s perspective. Simulation results on both synthetic and real-world networks have demonstrated the effectiveness of the proposed method.