A Self-Adaptive Evolutionary Deception Framework for Community Structure

The rapid development of community detection algorithms, while serving users in social networks, also brings about certain privacy problems. In this work, we study community deception, which aims to counter malicious community detection attacks by imperceptibly modifying a small part of the connections. However, it is computationally challenging to find an optimal edge set since it is an NP-hard problem. To address this issue, we propose a self-adaptive evolutionary deception (SAEP) framework. In SAEP, a novel fitness function that is able to capture local and global community change is being proposed. SAEP also provides a well-designed initialization mechanism to reduce the size of the solution space. In addition, we assign an indicator to each gene to reflect its strength within the chromosome that it belongs to, thereby a set of self-adaptive operations can be defined to enhance the algorithm's stability and efficacy. Furthermore, we define a new 'edge distance' to conserve the limited modification resource on the graph. In the experiment, the proposed method is tested against different community detection methods using various real-world datasets, and the experimental results demonstrate that SAEP improves significantly over state-of-the-art approaches in terms of effectiveness.