Research map / Graph learning / Graph self-supervised learning
N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use N-Gram Graph instead of ECFP with RF | RF · Delaney test | RMSE: 1.251 -> 0.802 | Helped |
| Use N-Gram Graph instead of ECFP with RF | RF · Malaria test | RMSE: 1.011 -> 1.011 | No clear effect |
| Use N-Gram Graph instead of ECFP with RF | RF · CEP test | RMSE: 1.667 -> 1.367 | Helped |
| Use N-Gram Graph instead of ECFP with XGB | XGB · Delaney test | RMSE: 1.120 -> 0.771 | Helped |
| Use N-Gram Graph instead of ECFP with XGB | XGB · Malaria test | RMSE: 0.998 -> 1.003 | Hurt |
| Use N-Gram Graph instead of ECFP with XGB | XGB · CEP test | RMSE: 1.442 -> 1.296 | Helped |
| Use N-Gram Graph instead of ECFP with DNN | DNN · Delaney test | RMSE: 1.40 -> 0.665 | Helped |
| Use N-Gram Graph instead of ECFP with DNN | DNN · Malaria test | RMSE: 1.13 -> 1.085 | Helped |
| Use N-Gram Graph instead of ECFP with DNN | DNN · CEP test | RMSE: 2.00 -> 1.359 | Helped |
Closest papers by meaning
- Pre-Training Graph Neural Networks on Molecules by Using Subgraph-Conditioned Graph Information Bottleneck (AAAI 2025 · similarity 0.88)
- HAGO-Net: Hierarchical Geometric Message Passing for Molecular Representation Learning (AAAI 2024 · similarity 0.88)
- MAGE: Model-Level Graph Neural Networks Explanations via Motif-based Graph Generation (ICLR 2025 · similarity 0.88)
- Enhancing Molecular Representation Learning Through the Combination of 3D and 2D Graph Machine Learning (Student Abstract) (AAAI 2025 · similarity 0.87)
- On the Scalability of GNNs for Molecular Graphs (NeurIPS 2024 · similarity 0.87)
- Representing Molecules as Random Walks Over Interpretable Grammars (ICML 2024 · similarity 0.87)
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