Network Dismantling Algorithm Based on Community Detection and Inverse Reinsertion of Edges

Network dismantling aims to find the minimal set of nodes or edges that, if removed, will break the network into small components and the scale of the giant connected component shall not exceed the pre-defined threshold. Traditional node-deleting based methods ignore the cost of deletion. In fact, when we delete a node, the corresponding edges linked to this node should also be deleted. The cost is different. Although traditional edge-deleting based methods take the cost into consideration, performance and efficiency need to be further improved. This paper proposes an edge-deleting based network dismantling algorithm, which contains two stages: community detection and inverse reinsertion of edges. In the first stage, the whole network is divided into different communities by using community detection algorithm and then edges between communities are removed to destroy the connectivity of communities. In the second stage, the strategy of inverse reinsertion of edges is used to destroy the connectivity within each community. Thus, we can dismantle the whole network into pieces. Experiment results on real-world and artificial networks show that, on one hand, our proposed method can dismantle networks by removing a smaller set of edges than that of other state-of-the-art methods. On the other hand, our proposed method exhibits stable performance with the variation of network scale, network structure and the threshold of network dismantling. © 2022, Chinese Institute of Electronics. All right reserved.