Multiscale Evolutionary Perturbation Attack on Community Detection

TL;DR

This paper introduces a novel evolutionary perturbation attack method to disrupt community detection in networks across multiple scales, effectively hiding communities and nodes with minimal link modifications, outperforming baseline methods.

Contribution

The paper formalizes community deception as a multi-scale attack problem and proposes EPA, a new evolutionary perturbation approach that effectively attacks various community detection algorithms.

Findings

EPA successfully attacks community detection algorithms at all three scales.

EPA outperforms baseline attack methods on synthetic and real-world networks.

EPA demonstrates good transferability to different community detection algorithms.

Abstract

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.