A neighborhood link sensitive dismantling method for social networks

Abstract

Network dismantling aims to find the minimal set of nodes that, if removed, will break the network into small components with their largest one limited to a certain threshold. This problem underlies many practical applications in various areas, such as bioinformatics, transportation and the Internet. There are two major kinds of network dismantling methods, i.e. centrality measure based methods and network decycling based methods. The former ignores the influence of the loop structure in network topology, while the latter massively deletes irrelevant nodes in the loop removal step, both resulting in poor performance. To solve these problems, this paper proposes a neighborhood link sensitive dismantling method for social networks. The proposed method contains two key steps, namely node deleting step and node re-inserting step. In node deleting step, a neighborhood link sensitive centrality measure is defined to identify the nodes that are really crucial to destroy the connectivity of network. In the following node re-inserting step, an appropriate greedy strategy is selected to refine the node set of network dismantling as much as possible to the theoretical optimal solution. Experimental results on real-world networks and synthetic networks demonstrate that the proposed method can break down networks by deleting only a smaller set of nodes, outperforming the existing state-of-the-art methods. Furthermore, our proposed method shows stable performance and strong adaptability on networks with different scales and structural characteristics.