Adversarial Attack on Community Detection by Hiding Individuals

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.Comment: In Proceedings of The Web Conference 2020, April 20-24, 2020, Taipei, Taiwan. 11 page

Network Members Can Hide from Group Centrality Measures

Group centrality measures are a generalization of standard centrality, designed to quantify the importance of not just a single node (as is the case with standard measures) but rather that of a group of nodes. Some nodes may have an incentive to evade such measures, i.e., to hide their actual importance, in order to conceal their true role in the network. A number of studies have been proposed in the literature to understand how nodes can rewire the network in order to evade standard centrality, but no study has focused on group centrality to date. We close this gap by analyzing four group centrality measures: degree, closeness, betweenness, and GED-walk. We show that an optimal way to rewire the network can be computed efficiently given the former measure, but the problem is NP-complete given closeness and betweenness. Moreover, we empirically evaluate a number of hiding strategies, and show that an optimal way to hide from degree group centrality is also effective in practice against the other measures. Altogether, our results suggest that it is possible to hide from group centrality measures based solely on the local information available to the group members about the network topology