SFR-GNN: Simple and Fast Robust GNNs against Structural Attacks

• URL: http://arxiv.org/abs/2408.16537v2
• Date: Sun, 1 Sep 2024 11:27:45 GMT
• Title: SFR-GNN: Simple and Fast Robust GNNs against Structural Attacks
• Authors: Xing Ai, Guanyu Zhu, Yulin Zhu, Yu Zheng, Gaolei Li, Jianhua Li, Kai Zhou,
• Abstract summary: Graph Neural Networks (GNNs) have demonstrated commendable performance for graph-structured data. GNNs are often vulnerable to adversarial structural attacks as embedding generation relies on graph topology. We propose an efficient defense method, called Simple and Fast Robust Graph Neural Network (SFR-GNN), supported by mutual information theory.
• Score: 13.30477801940754
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph Neural Networks (GNNs) have demonstrated commendable performance for graph-structured data. Yet, GNNs are often vulnerable to adversarial structural attacks as embedding generation relies on graph topology. Existing efforts are dedicated to purifying the maliciously modified structure or applying adaptive aggregation, thereby enhancing the robustness against adversarial structural attacks. It is inevitable for a defender to consume heavy computational costs due to lacking prior knowledge about modified structures. To this end, we propose an efficient defense method, called Simple and Fast Robust Graph Neural Network (SFR-GNN), supported by mutual information theory. The SFR-GNN first pre-trains a GNN model using node attributes and then fine-tunes it over the modified graph in the manner of contrastive learning, which is free of purifying modified structures and adaptive aggregation, thus achieving great efficiency gains. Consequently, SFR-GNN exhibits a 24%--162% speedup compared to advanced robust models, demonstrating superior robustness for node classification tasks.

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