Research map / Graph learning / Graph algorithms and clustering
Matching Node Embeddings for Graph Similarity
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| compare graphs via EMD between eigenvector node embeddings instead of optimal assignment | graph similarity · NCI1 (unlabeled) | accuracy: 67.81 -> 72.65 | Helped |
| pyramid match kernel on node embeddings vs local-structure kernels | graph kernels · unlabeled graphs, 6 datasets | accuracy: 63.67 -> 75.55 (D&D vs GR) | Helped |
| add WL relabeling to pyramid match (WL PM) (ablation) | PM · NCI1 (labeled) | accuracy: 72.91 -> 86.40 | Helped |
| WL PM vs WL SP | labeled graph kernels · ENZYMES (labeled) | accuracy: 61.00 vs 55.55 | Hurt |
| use PM instead of EMD for scalability | embedding-based similarity · D&D | runtime: > 1 day -> 41.23” | Helped |
Closest papers by meaning
- Rethinking Kernel Methods for Node Representation Learning on Graphs (NeurIPS 2019 · similarity 0.91)
- Graph Filtration Kernels (AAAI 2022 · similarity 0.90)
- Learning-Based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matching (AAAI 2020 · similarity 0.90)
- Exploring Consistency in Graph Representations: from Graph Kernels to Graph Neural Networks (NeurIPS 2024 · similarity 0.88)
- Graph Minimum Factor Distance and Its Application to Large-Scale Graph Data Clustering (ICML 2025 · similarity 0.87)
- Graph Classification via Reference Distribution Learning: Theory and Practice (NeurIPS 2024 · similarity 0.86)
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