Abstract
Graph Neural Networks (GNNs for short), a generalization of neural networks to graph-structured data, performance good in closed setting with perfect data for variety tasks, including node classification, link prediction and graph classification. However, GNNs are vulnerable to adversarial attacks, i.e., a small perturbation to the graph structure and node features in wild setting can lead to non-trivial performance degradation. Non-robustness is one of the main obstacle to applying GNNs in the wild. In this work, we focus on one of the most popular GNNs, Graph Convolutional Networks (GCN for short), and propose Stochastic Activation GCN (SA-GCN for short) to improve the robustness of GCN models. More specifically, we propose building a roust model by directly introducing a regularization term to the objective function and maximizing the feature distribution variance. Extensive experiments show that this simple design makes SA-GCN achieving significantly improved robustness against adversarial attacks. Moreover, our approach generalizes well and can be equipped with various models. Conducted empirical experiments demonstrate the effectiveness of SA-GCN.
Supported by Huxiang Youth Talent Support Program (No. 2021RC3076), and Training Program for Excellent Young Innovators of Changsha (No. KQ2009009).
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Yu, Z., Yang, H., Wang, L., Sun, L., Zhou, Y. (2022). Towards Robust Graph Convolution Network via Stochastic Activation. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2022. Lecture Notes in Computer Science, vol 13345. Springer, Cham. https://doi.org/10.1007/978-3-031-09726-3_15
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