Evasion Attacks to Graph Neural Networks via Influence Function

by   Binghui Wang, et al.

Graph neural networks (GNNs) have achieved state-of-the-art performance in many graph-related tasks, e.g., node classification. However, recent works show that GNNs are vulnerable to evasion attacks, i.e., an attacker can slightly perturb the graph structure to fool GNN models. Existing evasion attacks to GNNs have several key drawbacks: 1) they are limited to attack two-layer GNNs; 2) they are not efficient; or/and 3) they need to know GNN model parameters. We address the above drawbacks in this paper and propose an influence-based evasion attack against GNNs. Specifically, we first introduce two influence functions, i.e., feature-label influence and label influence, that are defined on GNNs and label propagation (LP), respectively. Then, we build a strong connection between GNNs and LP in terms of influence. Next, we reformulate the evasion attack against GNNs to be related to calculating label influence on LP, which is applicable to multi-layer GNNs and does not need to know the GNN model. We also propose an efficient algorithm to calculate label influence. Finally, we evaluate our influence-based attack on three benchmark graph datasets. Our experimental results show that, compared to state-of-the-art attack, our attack can achieve comparable attack performance, but has a 5-50x speedup when attacking two-layer GNNs. Moreover, our attack is effective to attack multi-layer GNNs.


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