Research map / Graph learning / GNN architectures
Hierarchical Graph Representation Learning with Differentiable Pooling
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add hierarchical differentiable pooling to GraphSage | GraphSage · Enzymes, D&D, Reddit-Multi-12k, Collab, Proteins | accuracy: 54.25 -> 62.53; 75.42 -> 80.64; 42.24 -> 47.08; 68.25 -> 75.48; 70.48 -> 76.25 | Mixed |
| Remove the link prediction side objective (ablation) | DiffPool · Enzymes, D&D, Reddit-Multi-12k, Collab, Proteins | accuracy: 62.53 -> 61.95; 80.64 -> 79.98; 47.08 -> 46.65; 75.48 -> 75.58; 76.25 -> 76.22 | Mixed |
| Replace learned assignments with deterministic graph clustering (ablation) | DiffPool · Enzymes, D&D, Reddit-Multi-12k, Collab, Proteins | accuracy: 62.53 -> 58.33; 80.64 -> 75.47; 47.08 -> 46.18; 75.48 -> 82.13; 76.25 -> 75.62 | Mixed |
| Add one DiffPool layer | S2V · Enzymes, D&D | accuracy: 61.10 -> 62.86; 78.92 -> 80.75 | Helped |
| Add two DiffPool layers | S2V · Enzymes, D&D | accuracy: 61.10 -> 63.33; 78.92 -> 82.07 | Helped |
| Add link prediction and entropy side objectives | DiffPool · Graph classification benchmarks | accuracy | Helped |
| Add the link prediction objective | DiffPool · Graph classification benchmarks | accuracy stability | Helped |
Closest papers by meaning
- SkipPool: Improved Sparse Hierarchical Graph Pooling with Differentiable Exploration (AAAI 2025 · similarity 0.90)
- Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter Tuning (ICLR 2025 · similarity 0.89)
- ENAHPool: The Edge-Node Attention-based Hierarchical Pooling for Graph Neural Networks (ICML 2025 · similarity 0.89)
- Geometry-Aware Edge Pooling for Graph Neural Networks (NeurIPS 2025 · similarity 0.88)
- VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPs (ICLR 2024 · similarity 0.88)
- Enhancing Graph Classification Robustness with Singular Pooling (NeurIPS 2025 · similarity 0.88)
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