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

Yassine ABBAHADDOU, Sofiane ENNADIR, Johannes F. Lutzeyer, Fragkiskos D. Malliaros, Michalis Vazirgiannis

Keywords: Robustness, Graph Neural Networks, Conditional Random Fields

TL;DR: We introduce a post-hoc approach that enhances the robustness of Graph Neural Networks during the inference.

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.

Primary Area: learning on graphs and other geometries & topologies

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Submission Number: 11159