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.
@article{arxiv.2411.05399,
title = {Post-Hoc Robustness Enhancement in Graph Neural Networks with Conditional Random Fields},
author = {Yassine Abbahaddou and Sofiane Ennadir and Johannes F. Lutzeyer and Fragkiskos D. Malliaros and Michalis Vazirgiannis},
journal= {arXiv preprint arXiv:2411.05399},
year = {2024}
}