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Found 3 papers, 3 papers with code
Graph Generation with Diffusion Mixture
Generation of graphs is a major challenge for real-world tasks that require understanding the complex nature of their non-Euclidean structures.
Graph Self-supervised Learning with Accurate Discrepancy Learning
Contrastive learning, while it can learn global graph-level similarities, its objective to maximize the similarity between two differently perturbed graphs may result in representations that cannot discriminate two similar graphs with different properties.
Edge Representation Learning with Hypergraphs
This dual hypergraph construction allows us to apply message-passing techniques for node representations to edges.
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