Reinforcement Learning-based Black-Box Evasion Attacks to Link Prediction in Dynamic Graphs

Link prediction in dynamic graphs (LPDG) is an important research problem that has diverse applications such as online recommendations, studies on disease contagion, organizational studies, etc. Various LPDG methods based on graph embedding and graph neural networks have been recently proposed and achieved state-of-the-art performance. In this paper, we study the vulnerability of LPDG methods and propose the first practical black-box evasion attack. Specifically, given a trained LPDG model, our attack aims to perturb the graph structure, without knowing to model parameters, model architecture, etc., such that the LPDG model makes as many wrong predicted links as possible. We design our attack based on a stochastic policy-based RL algorithm. Moreover, we evaluate our attack on three real-world graph datasets from different application domains. Experimental results show that our attack is both effective and efficient.

Evasion Attacks to Graph Neural Networks via Influence Function

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.

Efficient Evasion Attacks to Graph Neural Networks via Influence Function

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 perturb the graph structure to fool trained GNN models. Existing evasion attacks to GNNs have two key drawbacks. First, perturbing the graph structure to fool GNN models is essentially a binary optimization problem, while it is often solved via approximate algorithms with sub-optimal solutions. Second, existing attacks are only applicable to two-layer GNNs. In this paper, we aim to address the above drawbacks and propose to attack GNNs via influence function, a completely different perspective from existing works. Specifically, we first build the connection between GNNs and label propagation in terms of influence function. Then, instead of solving an approximate algorithm, we reformulate the attack to be related to (label) influence, which is applicable to multi-layer GNNs and whose solution can be calculated directly. We evaluate our attack on various benchmark graph datasets. Experimental results demonstrate that, compared to state-of-the-art attack, our attack can achieve higher attack success rate and has a 10-100x speedup when attacking two-layer GNNs. Moreover, our attack is also very effective to attack multi-layer GNNs.