Research map / Graph learning / GNN architectures
Information Gain Propagation: a New Way to Graph Active Learning with Soft Labels
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use relaxed correctness queries, soft labels, and information gain propagation for active selection | GNN active learning with GCN · Cora | test accuracy (%): 84.2(±0.3) -> 86.4(±0.6) | Helped |
| use relaxed correctness queries, soft labels, and information gain propagation for active selection | GNN active learning with GCN · Citeseer | test accuracy (%): 74.2(±0.3) -> 75.8(±0.3) | Helped |
| use relaxed correctness queries, soft labels, and information gain propagation for active selection | GNN active learning with GCN · PubMed | test accuracy (%): 81.8(±0.2) -> 83.6(±0.5) | Helped |
| use relaxed correctness queries, soft labels, and information gain propagation for active selection | GNN active learning with GCN · Reddit | test accuracy (%): 92.5(±0.1) -> 93.4(±0.2) | Helped |
| use relaxed correctness queries, soft labels, and information gain propagation for active selection | GNN active learning with GCN · ogbn-arxiv | test accuracy (%): 70.3(±0.2) -> 70.9(±0.3) | Helped |
| add training on soft labels (ablation) | IGP · PubMed | test accuracy (%): 80.7(±0.5) -> 83.6(±0.5) | Helped |
| add information gain propagation to node selection (ablation) | IGP · PubMed | test accuracy (%): 80.4(±0.4) -> 83.6(±0.5) | Helped |
| use influence magnitude rather than equal influence for all nodes (ablation) | IGP · PubMed | test accuracy (%): 81.5(±0.6) -> 83.6(±0.5) | Helped |
| use normalized soft labels instead of uniform soft labels (ablation) | IGP · PubMed | test accuracy (%): 81.3(±0.4) -> 83.6(±0.5) | Helped |
| remove propagation from information gain selection (ablation) | IGP · Reddit | test accuracy (%): 93.4(±0.2) -> 91.7(±0.3) | Hurt |
| increase the filtering degree from 0 to 15 (ablation) | IGP · Reddit | test accuracy (%): 93.6(±0.3) -> 93.4(±0.2) | Hurt |
| increase weak-label loss weight alpha from 0.5 to 2 (ablation) | IGP · PubMed | test accuracy (%): 83.5(±0.5) -> 83.4(±0.6) | No clear effect |
Closest papers by meaning
- Robust Offline Active Learning on Graphs (NeurIPS 2024 · similarity 0.87)
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- Preference-driven Knowledge Distillation for Few-shot Node Classification (NeurIPS 2025 · similarity 0.85)
- Beyond the Golden Teacher: Enhancing Graph Learning through LLM-GNN Co-teaching (NeurIPS 2026 · similarity 0.85)
- Label Attentive Distillation for GNN-Based Graph Classification (AAAI 2024 · similarity 0.85)
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