Research map / Graph learning / Graph self-supervised learning
Graph of Graphs: A New Knowledge Representation Mechanism for Graph Learning (Student Abstract)
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Represent graphs as nodes in a GoG using vertex-edge overlap distance | GCN graph classification · MUTAG, original labels | accuracy: 75.00 (6.00) -> 88.95 (5.80) | Helped |
| Represent graphs as nodes in a GoG using lambda distance | GCN graph classification · MUTAG, original labels | accuracy: 75.00 (6.00) -> 87.37 (7.51) | Helped |
| Represent graphs as nodes in a GoG using Frobenius norm distance | GCN graph classification · MUTAG, original labels | accuracy: 75.00 (6.00) -> 88.95 (5.62) | Helped |
| Represent graphs as nodes in a GoG using vertex-edge overlap distance | GCN graph classification · MUTAG, attacked labels | accuracy: 55.00 (10.94) -> 83.16 (7.21) | Helped |
| Represent graphs as nodes in a GoG using lambda distance | GCN graph classification · MUTAG, attacked labels | accuracy: 55.00 (10.94) -> 82.10 (6.66) | Helped |
| Represent graphs as nodes in a GoG using Frobenius norm distance | GCN graph classification · MUTAG, attacked labels | accuracy: 55.00 (10.94) -> 84.21 (9.35) | Helped |
| Represent graphs as nodes in a GoG using vertex-edge overlap distance | GCN graph classification · BZR, original labels | accuracy: 81.75 (5.28) -> 82.44 (3.91) | Helped |
| Represent graphs as nodes in a GoG using lambda distance | GCN graph classification · BZR, original labels | accuracy: 81.75 (5.28) -> 82.46 (6.82) | Helped |
| Represent graphs as nodes in a GoG using Frobenius norm distance | GCN graph classification · BZR, original labels | accuracy: 81.75 (5.28) -> 84.44 (6.00) | Helped |
| Represent graphs as nodes in a GoG using vertex-edge overlap distance | GCN graph classification · BZR, attacked labels | accuracy: 48.33 (13.37) -> 80.73 (5.80) | Helped |
| Represent graphs as nodes in a GoG using lambda distance | GCN graph classification · BZR, attacked labels | accuracy: 48.33 (13.37) -> 81.46 (4.77) | Helped |
| Represent graphs as nodes in a GoG using Frobenius norm distance | GCN graph classification · BZR, attacked labels | accuracy: 48.33 (13.37) -> 81.22 (5.98) | Helped |
Closest papers by meaning
- Core Knowledge Learning Framework for Graph (AAAI 2025 · similarity 0.91)
- Graph Classification via Reference Distribution Learning: Theory and Practice (NeurIPS 2024 · similarity 0.89)
- Hypergraph-enhanced Dual Semi-supervised Graph Classification (ICML 2024 · similarity 0.88)
- Node Identifiers: Compact, Discrete Representations for Efficient Graph Learning (ICLR 2025 · similarity 0.88)
- Learning Graph Representation via Graph Entropy Maximization (ICML 2024 · similarity 0.88)
- Toward a Unified Statistical Theory of Unsupervised Pretraining and Supervised Neural Knowledge Graph Learning (NeurIPS 2026 · similarity 0.87)
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