Cluster Attack: Query-based Adversarial Attacks on Graphs with Graph-Dependent Priors

TL;DR

This paper introduces Cluster Attack, a query-based black-box adversarial attack on graph neural networks that injects fake nodes to degrade node classification performance while minimizing impact on other nodes.

Contribution

It formulates the attack as a graph clustering problem and proposes a novel similarity metric, enabling effective attacks with few queries.

Findings

Successfully fools GNNs with limited queries.

Effective in degrading node classification accuracy.

Operates in a practical, unnoticeable manner.

Abstract

While deep neural networks have achieved great success in graph analysis, recent work has shown that they are vulnerable to adversarial attacks. Compared with adversarial attacks on image classification, performing adversarial attacks on graphs is more challenging because of the discrete and non-differential nature of the adjacent matrix for a graph. In this work, we propose Cluster Attack -- a Graph Injection Attack (GIA) on node classification, which injects fake nodes into the original graph to degenerate the performance of graph neural networks (GNNs) on certain victim nodes while affecting the other nodes as little as possible. We demonstrate that a GIA problem can be equivalently formulated as a graph clustering problem; thus, the discrete optimization problem of the adjacency matrix can be solved in the context of graph clustering. In particular, we propose to measure the similarity between victim nodes by a metric of Adversarial Vulnerability, which is related to how the victim nodes will be affected by the injected fake node, and to cluster the victim nodes accordingly. Our attack is performed in a practical and unnoticeable query-based black-box manner with only a few nodes on the graphs that can be accessed. Theoretical analysis and extensive experiments demonstrate the effectiveness of our method by fooling the node classifiers with only a small number of queries.