Unnoticeable Community Deception via Multi-objective Optimization

--Community detection in graphs is crucial for understanding the organization of nodes into densely connected clusters. While numerous strategies have been developed to identify these clusters, the success of community detection can lead to privacy and information security concerns, as individuals may not want their personal information exposed. T o address this, community deception methods have been proposed to reduce the effectiveness of detection algorithms. Nevertheless, several limitations, such as the rationality of evaluation metrics and the unnoticeability of attacks, have been ignored in current deception methods. Therefore, in this work, we first investigate the limitations of the widely used deception metric, i.e., the decrease of modularity, through empirical studies. Then, we propose a new deception metric, and combine this new metric together with the attack budget to model the unnoticeable community deception task as a multi-objective optimization problem. T o further improve the deception performance, we propose two variant methods by incorporating the degree-biased and community-biased candidate node selection mechanisms. Extensive experiments on three benchmark datasets demonstrate the superiority of the proposed community deception strategies. RAPHS or networks, which consist of a series of nodes and links, have been widely employed to model complex relationships in modern social systems. The typical examples of networks include social networks which illustrate the social relationships between people, financial networks which represent the transaction records between accounts, power networks which indicate the transmission relationship between power nodes, among others [1]-[4]. In these years, significant efforts have been put into investigating the abundant information behind graph-based systems, such as community detection for clustering nodes in the graph into different groups, node classification for identifying the labels of nodes, link prediction for predicting possible future connections between nodes, etc [5]-[7]. Specifically, in this work, we focus on the task of community detection. Junyuan Fang and Chi K. Tse are with the Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR, China. Huimin Liu and Y ueqi Peng are with the School of Computer Science and Engineering, Sun Y at-sen University, Guangzhou 510006, China.