GA-Based Q-Attack on Community Detection

Community detection plays an important role in social networks, since it can help to naturally divide the network into smaller parts so as to simplify network analysis. However, on the other hand, it arises the concern that individual information may be overmined, and the concept community deception...

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Veröffentlicht in:IEEE transactions on computational social systems Jg. 6; H. 3; S. 491 - 503
Hauptverfasser: Chen, Jinyin, Chen, Lihong, Chen, Yixian, Zhao, Minghao, Yu, Shanqing, Xuan, Qi, Yang, Xiaoniu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.06.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Adversarial network
Algorithms
Communities
community detection
community detection attack
Cyberspace
Deception
Detection algorithms
Fitness
genetic algorithm
Genetic algorithms
Modularity
Network analysis
Optimization
Privacy
privacy protection
Rewiring
Security
social network
Social networking (online)
Social networks
ISSN:2329-924X, 2373-7476
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Zusammenfassung:Community detection plays an important role in social networks, since it can help to naturally divide the network into smaller parts so as to simplify network analysis. However, on the other hand, it arises the concern that individual information may be overmined, and the concept community deception has been proposed to protect individual privacy on social networks. Here, we introduce and formalize the problem of community detection attack and develop efficient strategies to attack community detection algorithms by rewiring a small number of connections, leading to privacy protection. In particular, we first give two heuristic attack strategies, i.e., Community Detection Attack (CDA) and Degree Based Attack (DBA), as baselines, utilizing the information of detected community structure and node degree, respectively. Then, we propose an attack strategy called "genetic algorithm (GA)-based Q-Attack," where the modularity <inline-formula> <tex-math notation="LaTeX">Q </tex-math></inline-formula> is used to design the fitness function. We launch community detection attack based on the above three strategies against six community detection algorithms on several social networks. By comparison, our Q-Attack method achieves much better attack effects than CDA and DBA, in terms of the larger reduction of both modularity <inline-formula> <tex-math notation="LaTeX">Q </tex-math></inline-formula> and normalized mutual information (NMI). In addition, we further take transferability tests and find that adversarial networks obtained by Q-Attack on a specific community detection algorithm also show considerable attack effects while generalized to other algorithms.
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ISSN:2329-924X
2373-7476
DOI:10.1109/TCSS.2019.2912801