Research map / Graph learning / GNN architectures
Identity-aware Graph Neural Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add identity-aware heterogeneous message passing | GCN on node clustering-coefficient prediction · ScaleFree | accuracy: 0.693±0.00 -> 0.985±0.01 | Helped |
| Add identity-aware heterogeneous message passing | SAGE on shortest-path-distance prediction · PROTEINS | accuracy: 0.722±0.01 -> 1.000±0.00 | Helped |
| Add cycle-count node features | SAGE on node clustering-coefficient prediction · SmallWorld | accuracy: 0.589±0.04 -> 0.994±0.00 | Helped |
| Use identity-aware heterogeneous message passing rather than pairwise node embeddings | GNN link prediction · PROTEINS | ROC AUC: 0.667±0.00 -> 0.886±0.02 | Helped |
| Add cycle-count node features | SAGE on node classification · CiteSeer | accuracy: 0.726±0.01 -> 0.742±0.01 | Helped |
| Use identity-aware heterogeneous message passing | GNN on real-world graph classification · ogbg-molhiv | ROC AUC: 0.762±0.03 -> 0.783±0.02 | Helped |
Closest papers by meaning
- Towards Dynamic Message Passing on Graphs (NeurIPS 2024 · similarity 0.89)
- Node Identifiers: Compact, Discrete Representations for Efficient Graph Learning (ICLR 2025 · similarity 0.89)
- Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellence (ICML 2025 · similarity 0.88)
- Beyond Message Passing: Neural Graph Pattern Machine (ICML 2025 · similarity 0.88)
- On the Expressivity and Sample Complexity of Node-Individualized Graph Neural Networks (NeurIPS 2024 · similarity 0.88)
- A Flexible, Equivariant Framework for Subgraph GNNs via Graph Products and Graph Coarsening (NeurIPS 2024 · similarity 0.88)
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