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Community detection, aiming to group nodes based on their connections, playsan important role in network analysis, since communities, treated asmeta-nodes, allow us to create a large-scale map of a network to simplify itsanalysis. However, for privacy reasons, we may want to prevent communities frombeing discovered in certain cases, leading to the topics on communitydeception. In this paper, we formalize this community detection attack problemin three scales, including global attack (macroscale), target community attack(mesoscale) and target node attack (microscale). We treat this as anoptimization problem and further propose a novel Evolutionary PerturbationAttack (EPA) method, where we generate adversarial networks to realize thecommunity detection attack. Numerical experiments validate that our EPA cansuccessfully attack network community algorithms in all three scales, i.e.,hide target nodes or communities and further disturb the community structure ofthe whole network by only changing a small fraction of links. By comparison,our EPA behaves better than a number of baseline attack methods on sixsynthetic networks and three real-world networks. More interestingly, althoughour EPA is based on the louvain algorithm, it is also effective on attackingother community detection algorithms, validating its good transferability.

Multiscale Evolutionary Perturbation Attack on Community Detection