Post-Hoc Robustness Enhancement in Graph Neural Networks with Conditional Random Fields

• URL: http://arxiv.org/abs/2411.05399v1
• Date: Fri, 08 Nov 2024 08:26:42 GMT
• Title: Post-Hoc Robustness Enhancement in Graph Neural Networks with Conditional Random Fields
• Authors: Yassine Abbahaddou, Sofiane Ennadir, Johannes F. Lutzeyer, Fragkiskos D. Malliaros, Michalis Vazirgiannis,
• Abstract summary: Graph Neural Networks (GNNs) have been shown to be vulnerable to adversarial attacks. This study introduces RobustCRF, a post-hoc approach aiming to enhance the robustness of GNNs at the inference stage.
• Score: 19.701706244728037
• License:
• Abstract: Graph Neural Networks (GNNs), which are nowadays the benchmark approach in graph representation learning, have been shown to be vulnerable to adversarial attacks, raising concerns about their real-world applicability. While existing defense techniques primarily concentrate on the training phase of GNNs, involving adjustments to message passing architectures or pre-processing methods, there is a noticeable gap in methods focusing on increasing robustness during inference. In this context, this study introduces RobustCRF, a post-hoc approach aiming to enhance the robustness of GNNs at the inference stage. Our proposed method, founded on statistical relational learning using a Conditional Random Field, is model-agnostic and does not require prior knowledge about the underlying model architecture. We validate the efficacy of this approach across various models, leveraging benchmark node classification datasets.

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