Research Article
Attacking Community Detectors: Mislead Detectors via Manipulating the Graph Structure
@INPROCEEDINGS{10.1007/978-3-030-99203-3_8, author={Kaibin Wan and Jiamou Liu and Yiwei Liu and Zijian Zhang and Bakhadyr Khoussainov}, title={Attacking Community Detectors: Mislead Detectors via Manipulating the Graph Structure}, proceedings={Mobile Computing, Applications, and Services. 12th EAI International Conference, MobiCASE 2021, Virtual Event, November 13--14, 2021, Proceedings}, proceedings_a={MOBICASE}, year={2022}, month={3}, keywords={Adversarial community detection Graph neural network Structural entropy}, doi={10.1007/978-3-030-99203-3_8} }- Kaibin Wan
Jiamou Liu
Yiwei Liu
Zijian Zhang
Bakhadyr Khoussainov
Year: 2022
Attacking Community Detectors: Mislead Detectors via Manipulating the Graph Structure
MOBICASE
Springer
DOI: 10.1007/978-3-030-99203-3_8
Abstract
Community detection has been widely studied from many different perspectives, which include heuristic approaches in the past and graph neural network in recent years. With increasing security and privacy concerns, community detectors have been demonstrated to be vulnerable. A slight perturbation to the graph data can greatly change the detection results. In this paper, we focus on dealing with a kind of attack on one of the communities by manipulating the graph structure. We formulate this case as target community problem. The big challenge to solve this problem is the universality on different detectors. For this, we define structural information gain (SIG) to guide the manipulation and design an attack algorithm named SIGM. We compare SIGM with some recent attacks on five graph datasets. Results show that our attack is effective on misleading community detector.
