Research map / Graph learning / GNN architectures
Fisher Information Embedding for Node and Graph Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use unsupervised FIE instead of Node2vec | Node classification · Cora | accuracy: 73.55 ± 0.07 -> 82.36±0.28 | Helped |
| Use unsupervised FIE instead of WWL | Node classification · Citeseer | accuracy: 66.60 ± 0.00 -> 72.02±1.34 | Helped |
| Use unsupervised FIE instead of Node2vec | Node classification · Pubmed | accuracy: 71.22 ± 1.04 -> 79.22±0.17 | Helped |
| Use unsupervised FIE instead of WWL | Node classification · Reddit | accuracy: 96.13 ± 0.04 -> 96.61±0.02 | Helped |
| Use unsupervised FIE instead of Node2vec | Node classification · ogbn-arxiv | accuracy: 70.67 ± 0.09 -> 72.17±0.07 | Helped |
| Use unsupervised FIE instead of Node2vec | Node classification · ogbn-products | accuracy: 74.77 ± 0.14 -> 79.24±0.11 | Helped |
| Use unsupervised FIE instead of GATv2 | Node classification · Cora | accuracy: 82.04 ± 0.82 -> 82.36±0.28 | Mixed |
| Use unsupervised FIE instead of GATv2 | Node classification · Citeseer | accuracy: 69.72 ± 1.03 -> 72.02±1.34 | Helped |
| Use unsupervised FIE instead of GAT | Node classification · Pubmed | accuracy: 79.14 ± 0.92 -> 79.22±0.17 | Mixed |
| Use unsupervised FIE instead of GATv2 | Node classification · Reddit | accuracy: 96.58 ± 0.01 -> 96.61±0.02 | Mixed |
| Use supervised FIE instead of GATv2 | Node classification · Citeseer | accuracy: 69.72 ± 1.03 -> 72.51±0.34 | Helped |
| Increase the number of Gaussian-mixture components above 1 (ablation) | FIE · Node classification benchmarks | validation accuracy | Helped |
Closest papers by meaning
- Watch Your Step: Learning Node Embeddings via Graph Attention (NeurIPS 2018 · similarity 0.93)
- Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking (ICLR 2018 · similarity 0.90)
- Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models (ICML 2025 · similarity 0.88)
- HONGAT: Graph Attention Networks in the Presence of High-Order Neighbors (AAAI 2024 · similarity 0.88)
- Subgraph Invariant Learning Towards Large-Scale Graph Node Classification (AAAI 2025 · similarity 0.88)
- NLGT: Neighborhood-based and Label-enhanced Graph Transformer Framework for Node Classification (AAAI 2025 · similarity 0.88)
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