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Found 5 papers, 4 papers with code
Dynamic Graph Information Bottleneck
Leveraged by the Information Bottleneck (IB) principle, we first propose the expected optimal representations should satisfy the Minimal-Sufficient-Consensual (MSC) Condition.
Poincaré Differential Privacy for Hierarchy-Aware Graph Embedding
Specifically, PoinDP first learns the hierarchy weights for each entity based on the Poincar\'e model in hyperbolic space.
Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalization
To the best of our knowledge, we are the first to study OOD generalization on dynamic graphs from the environment learning perspective.
Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node Classification
We find that training labeled nodes with different hierarchical properties have a significant impact on the node classification tasks and confirm it in our experiments.
Position-aware Structure Learning for Graph Topology-imbalance by Relieving Under-reaching and Over-squashing
Topology-imbalance is a graph-specific imbalance problem caused by the uneven topology positions of labeled nodes, which significantly damages the performance of GNNs.
