Self-Guided Robust Graph Structure Refinement
CoRR(2024)
摘要
Recent studies have revealed that GNNs are vulnerable to adversarial attacks.
To defend against such attacks, robust graph structure refinement (GSR) methods
aim at minimizing the effect of adversarial edges based on node features, graph
structure, or external information. However, we have discovered that existing
GSR methods are limited by narrowassumptions, such as assuming clean node
features, moderate structural attacks, and the availability of external clean
graphs, resulting in the restricted applicability in real-world scenarios. In
this paper, we propose a self-guided GSR framework (SG-GSR), which utilizes a
clean sub-graph found within the given attacked graph itself. Furthermore, we
propose a novel graph augmentation and a group-training strategy to handle the
two technical challenges in the clean sub-graph extraction: 1) loss of
structural information, and 2) imbalanced node degree distribution. Extensive
experiments demonstrate the effectiveness of SG-GSR under various scenarios
including non-targeted attacks, targeted attacks, feature attacks, e-commerce
fraud, and noisy node labels. Our code is available at
https://github.com/yeonjun-in/torch-SG-GSR.
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