Adversarial Attack on Community Detection by Hiding Individuals
This work addresses privacy protection and camouflage understanding in social and transaction networks, representing an incremental extension of adversarial attacks to community detection.
The paper tackles the problem of hiding targeted individuals from deep graph community detection models by generating adversarial graphs, achieving successful attacks in black-box settings with transferability to other models.
It has been demonstrated that adversarial graphs, i.e., graphs with imperceptible perturbations added, can cause deep graph models to fail on node/graph classification tasks. In this paper, we extend adversarial graphs to the problem of community detection which is much more difficult. We focus on black-box attack and aim to hide targeted individuals from the detection of deep graph community detection models, which has many applications in real-world scenarios, for example, protecting personal privacy in social networks and understanding camouflage patterns in transaction networks. We propose an iterative learning framework that takes turns to update two modules: one working as the constrained graph generator and the other as the surrogate community detection model. We also find that the adversarial graphs generated by our method can be transferred to other learning based community detection models.