Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation. (arXiv:2201.07986v1 [cs.LG])

Graph contrastive learning is the state-of-the-art unsupervised graph
representation learning framework and has shown comparable performance with
supervised approaches. However, evaluating whether the graph contrastive
learning is robust to adversarial attacks is still an open problem because most
existing graph adversarial attacks are supervised models, which means they
heavily rely on labels and can only be used to evaluate the graph contrastive
learning in a specific scenario. For unsupervised graph representation methods
such as graph contrastive learning, it is difficult …

arxiv graph unsupervised