Reliable Representations Make A Stronger Defender: Unsupervised Structure Refinement for Robust GNN. (arXiv:2207.00012v3 [cs.LG] UPDATED)

Benefiting from the message passing mechanism, Graph Neural Networks (GNNs)
have been successful on flourish tasks over graph data. However, recent studies
have shown that attackers can catastrophically degrade the performance of GNNs
by maliciously modifying the graph structure. A straightforward solution to
remedy this issue is to model the edge weights by learning a metric function
between pairwise representations of two end nodes, which attempts to assign low
weights to adversarial edges. The existing methods use either raw features …

defender