Graph Contrastive Learning with Global Adversarial Negative Examples
Graph contrastive learning,a successful unsupervised node representation method,aims to learn node rep-resentations by pulling the augmented versions of the node together(positive examples),while pushing it with other nodes apart(negative examples).One key component of graph contrastive learning is the choice of negative exam-ples,and existing methods fail accurately finding the challengeable negative examples that are critical to the model.We propose to learn a global negative example for all the nodes,through adversarial learning.Extensive experiment results demonstrate both the efficiency and effectiveness of the proposed model.
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