Research map / Graph learning / Graph algorithms and clustering
GraphZoom: A Multi-level Spectral Approach for Accurate and Scalable Graph Embedding
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use GraphZoom with graph fusion, spectral coarsening, and refinement instead of the base embedding model | DeepWalk · Cora, Citeseer, and Pubmed | accuracy: relative gain of 8.3%, 10.4%, and 19.4% on Cora, Pubmed, and Citeseer | Helped |
| Use GraphZoom rather than the base DGI embedding | DGI · Cora, Citeseer, and Pubmed | accuracy | Mixed |
| Use GraphZoom with GraphSAGE rather than GraphSAGE on the original graph | GraphSAGE · PPI and Reddit | micro-F1: 3.4% and 3.3% | Helped |
| Add graph fusion of node attributes and topology (ablation) | GraphZoom · Cora, Citeseer, and Pubmed | classification accuracy | Helped |
| Replace MILE coarsening with spectral coarsening (ablation) | GraphZoom · Cora, Citeseer, and Pubmed | classification accuracy | Helped |
| Replace MILE refinement with graph-filter refinement (ablation) | GraphZoom · Cora, Citeseer, and Pubmed | classification accuracy | Helped |
| Combine spectral coarsening and graph-filter refinement (ablation) | GraphZoom · Cora, Citeseer, and Pubmed | classification accuracy | Helped |
| Increase the coarsening level (ablation) | GraphZoom · Cora, Citeseer, Pubmed, PPI, and Reddit | classification accuracy or micro-F1 | Mixed |
| Increase the coarsening level | MILE · Pubmed | accuracy: 0.708 to 0.618 | Hurt |
| Tune fusion weight beta to balance attribute and topology information (ablation) | GraphZoom · Cora, Citeseer, and Pubmed | classification accuracy | Mixed |
| Add graph fusion (ablation) | GraphZoom coarsening and refinement kernels · Cora, Citeseer, and Pubmed | CPU time | Hurt |
Closest papers by meaning
- A Provable Framework of Learning Graph Embeddings via Summarization (AAAI 2023 · similarity 0.91)
- Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking (ICLR 2018 · similarity 0.90)
- Watch Your Step: Learning Node Embeddings via Graph Attention (NeurIPS 2018 · similarity 0.90)
- Weighted Embeddings for Low-Dimensional Graph Representation (AAAI 2025 · similarity 0.89)
- Learning Representations for Hierarchies with Minimal Support (NeurIPS 2024 · similarity 0.88)
- TopER: Topological Embeddings in Graph Representation Learning (NeurIPS 2025 · similarity 0.88)
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