Abstract: Hard negative mining has shown effective in enhancing self-supervised contrastive learning (CL) on diverse data types, including graph CL (GCL). The existing hardness-aware CL methods ...
Abstract: Contrastive learning gains remarkable success in graph representation learning through imposing two properties to node representations, i.e., aligning representations of similar nodes in ...
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