  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 over-mined, and the concept community deception
thus is 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 individual 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.
And then we propose a Genetic Algorithm (GA) based Q-Attack, where the
modularity Q is used to design the fitness function. We launch community
detection attack based on the above three strategies against three modularity
based community detection algorithms on two 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 Q and Normalized Mutual Information
(NMI). Besides, we find that the adversarial networks obtained by Q-Attack on a
specific community detection algorithm can be still effective on others, no
matter whether they are modularity based or not, indicating its strong
transferability.
