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Graph Few-Shot Learning via Knowledge Transfer
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use graph-structured prototypes instead of mean-pooled prototypes (ablation) | GFL · Collaboration, Reddit, Citation, Pubmed | accuracy: 80.49± 0.55% -> 83.79± 0.39%; 60.46±0.67% -> 63.14±0.51%; 95.12±0.17% -> 96.51± 0.31%; 87.90±0.54% -> 89.37±0.41% | Helped |
| add the hierarchical graph representation gate (ablation) | GFL · Collaboration, Reddit, Citation, Pubmed | accuracy: 82.63± 0.45% -> 83.79± 0.39%; 61.99± 0.27% -> 63.14±0.51%; 95.33±0.35% -> 96.51± 0.31%; 88.15± 0.55% -> 89.37±0.41% | Helped |
| use hierarchical rather than flat graph representation (ablation) | GFL · Collaboration, Reddit, Citation, Pubmed | accuracy: 83.45±0.41% -> 83.79± 0.39%; 62.55± 0.65% -> 63.14±0.51%; 95.76± 0.37% -> 96.51± 0.31%; 89.08± 0.47% -> 89.37±0.41% | Helped |
| add graph reconstruction loss (ablation) | GFL · Collaboration, Reddit, Citation, Pubmed | accuracy: 82.98±0.37% -> 83.79± 0.39%; 62.58±0.47% -> 63.14±0.51%; 95.63±0.27% -> 96.51± 0.31%; 89.11±0.43% -> 89.37±0.41% | Helped |
| use attention rather than mean pooling to aggregate graph levels (ablation) | GFL · Collaboration and Reddit versus Citation and Pubmed | accuracy: 83.51± 0.38% -> 83.79± 0.39%; 62.66±0.57% -> 63.14±0.51%; 96.51±0.31% -> 95.85±0.26%; 89.37±0.41% -> 88.96±0.43% | Mixed |
| vary the relational-structure threshold μ among 0.5, 0.6, 0.7, and 0.8 (ablation) | GFL · Collaboration, Reddit, Citation, Pubmed | accuracy: Collaboration: 83.79% (μ=0.5), 83.26% (μ=0.6), 83.31% (μ=0.7), 83.14% (μ=0.8); Reddit: 63.14%, 63.43%, 63.17%, 63.21%; C | Mixed |
| replace Top-k CN with Jaccard, Adamic-Adar, or PageRank for relational similarity (ablation) | GFL · Collaboration, Reddit, Citation, Pubmed | accuracy: Jaccard: 82.98%, 62.71%, 95.18%, 88.91%; Adamic-Adar: 83.70%, 62.87%, 95.49%, 89.21%; PageRank: 84.14%, 63.08%, 95.93%, | Mixed |
| replace inner-product distance with cosine distance (ablation) | GFL · Collaboration, Reddit, Citation, Pubmed | accuracy: 83.79% -> 84.02%; 63.14% -> 62.95%; 96.51% -> 96.02%; 89.37% -> 89.25% | Mixed |
Closest papers by meaning
- Unlocking the Potential of Black-box Pre-trained GNNs for Graph Few-shot Learning (AAAI 2025 · similarity 0.90)
- Preference-driven Knowledge Distillation for Few-shot Node Classification (NeurIPS 2025 · similarity 0.89)
- Fast Graph Sharpness-Aware Minimization for Enhancing and Accelerating Few-Shot Node Classification (NeurIPS 2024 · similarity 0.88)
- Leveraging Large Language Models for Node Generation in Few-Shot Learning on Text-Attributed Graphs (AAAI 2025 · similarity 0.88)
- Normalize Then Propagate: Efficient Homophilous Regularization for Few-Shot Semi-Supervised Node Classification (AAAI 2025 · similarity 0.88)
- Graph Few-Shot Learning via Adaptive Spectrum Experts and Cross-Set Distribution Calibration (NeurIPS 2025 · similarity 0.88)
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