EGALA: Efficient Gradient Approximation for Large-scale Graph Adversarial Attack
Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
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Keywords: Graph adversarial attack
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Abstract: Graph Neural Networks (GNNs) have emerged as powerful tools for graph representation learning. However, their vulnerability to adversarial attacks underscores the importance of gaining a deeper understanding of techniques in graph adversarial attacks. Existing attack methods have demonstrated that it is possible to deteriorate the predictions of GNNs by injecting a small number of edges, but they often suffer from poor scalability due to the need of computing/storing gradients on a quadratic number of entries in the adjacency matrix. In this paper, we propose EGALA, a novel approach for conducting large-scale graph adversarial attacks. By showing the derivative of linear graph neural networks can be approximated by the inner product of two matrices, EGALA leverages efficient Approximate Nearest Neighbor Search (ANNS) techniques to identify entries with dominant gradients in sublinear time, offering superior attack capabilities, reduced memory and time consumption, and enhanced scalability. We conducted comprehensive experiments across various datasets to demonstrate the outstanding performance of our model compared with the state-of-the-art methods.
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Submission Number: 3975
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