  Community detection, aiming to group nodes based on their connections, plays
an important role in network analysis, since communities, treated as
meta-nodes, allow us to create a large-scale map of a network to simplify its
analysis. However, for privacy reasons, we may want to prevent communities from
being discovered in certain cases, leading to the topics on community
deception. In this paper, we formalize this community detection attack problem
in three scales, including global attack (macroscale), target community attack
(mesoscale) and target node attack (microscale). We treat this as an
optimization problem and further propose a novel Evolutionary Perturbation
Attack (EPA) method, where we generate adversarial networks to realize the
community detection attack. Numerical experiments validate that our EPA can
successfully attack network community algorithms in all three scales, i.e.,
hide target nodes or communities and further disturb the community structure of
the whole network by only changing a small fraction of links. By comparison,
our EPA behaves better than a number of baseline attack methods on six
synthetic networks and three real-world networks. More interestingly, although
our EPA is based on the louvain algorithm, it is also effective on attacking
other community detection algorithms, validating its good transferability.
