LSHA: A Local Structure-Based Community Detection Attack Heuristic Approach

Haipeng Yang, Lin Chen, Fan Cheng, Jianfeng Qiu, Lei Zhang

Abstract: The abuse of community detection algorithms may bring the risk of privacy leakage. To protect personal privacy in complex networks, community detection attack algorithms are proposed, which can hide the true community structure of the whole network from the community detection algorithms by adding and deleting subtle edges. However, most of the existing algorithms perform attack based on a community structure so that a specific community detection method is usually adopted for obtaining the communities, which causes the algorithms to not perform well when the attacked community detection algorithm is unknown. To this end, a local structure-based community detection attack heuristic approach (LSHA) is proposed in this article, where the local structures, including several nodes with dense connections instead of the whole community structures, are considered. Unlike the whole community structures obtained by different community detection algorithms, which are usually different, the nodes in such a local structure are often assigned into the same community so that the attack is more general for different community detection algorithms. Specifically, in LSHA, a local structure selection strategy is proposed to maximize the attack effect, which selects two local structures for rewiring attack. Furthermore, two metrics, i.e., edge vulnerability and node entropy, are also suggested to select the nodes and edges for attack. In the experiments, the proposed LSHA is compared with five state-of-the-art attack algorithms. The experimental results against five representative community detection algorithms on nine real-world networks show that the proposed algorithm LSHA achieves good performance on both the attack effectiveness and the efficiency.