TY - GEN
T1 - Attacking Community Detectors
T2 - 12th EAI International Conference on Mobile Computing, Applications and Services, MobiCASE 2021
AU - Wan, Kaibin
AU - Liu, Jiamou
AU - Liu, Yiwei
AU - Zhang, Zijian
AU - Khoussainov, Bakhadyr
N1 - Publisher Copyright:
© 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Adversarial community detection
KW - Graph neural network
KW - Structural entropy
UR - http://www.scopus.com/inward/record.url?scp=85127641568&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-99203-3_8
DO - 10.1007/978-3-030-99203-3_8
M3 - Conference contribution
AN - SCOPUS:85127641568
SN - 9783030992026
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 112
EP - 128
BT - Mobile Computing, Applications, and Services - 12th EAI International Conference, MobiCASE 2021, Proceedings
A2 - Deng, Shuiguang
A2 - Zomaya, Albert
A2 - Li, Ning
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 13 November 2021 through 14 November 2021
ER -