Robust Unsupervised Graph Representation Learning via Mutual Information Maximization. (arXiv:2201.08557v1 [cs.LG])

Web: http://arxiv.org/abs/2201.08557

Recent studies have shown that GNNs are vulnerable to adversarial attack.
Thus, many approaches are proposed to improve the robustness of GNNs against
adversarial attacks. Nevertheless, most of these methods measure the model
robustness based on label information and thus become infeasible when labels
information is not available. Therefore, this paper focuses on robust
unsupervised graph representation learning. In particular, to quantify the
robustness of GNNs without label information, we propose a robustness measure,
named graph representation robustness (GRR), to …

arxiv graph information learning unsupervised