Robustness of deep learning models on graphs: A survey

Abstract

Abstract Machine learning (ML) technologies have achieved significant success in various downstream tasks, e.g., node classification, link prediction, community detection, graph classification and graph clustering. However, many studies have shown that the models built upon ML technologies are vulnerable to noises and adversarial attacks. A number of works have studied the robust models against noise or adversarial examples in image domains and text processing domains, however, it is more challenging to learn robust models in graph domains. Adding noises or perturbations on graph data will make the robustness even harder to enhance – the noises and perturbations of edges or node attributes are easy to propagate to other neighbors via the relational information on a graph. In this paper, we investigate and summarize the existing works that study the robust deep learning models against adversarial attacks or noises on graphs, namely the robust learning (models) on graphs. Specifically, we first provide some robustness evaluation metrics of model robustness on graphs. Then, we comprehensively provide a taxonomy which groups robust models on graphs into five categories: anomaly detection, adversarial training, pre-processing, attention mechanism, and certifiable robustness. Besides, we emphasize some promising future directions in learning robust models on graphs. Hopefully, our works can offer insights for the relevant researchers, thus providing assistance for their studies.