A modularized repository for Spatio-Temporal Causal Learning under
- ADNI (https://adni.loni.usc.edu/)
- IRMA (https://www.cell.com/supplemental/S0092-8674(09)00156-1)
- DREAM3 (https://gnw.sourceforge.net/dreamchallenge.html#dream3challenge)
- Netsim (https://www.fmrib.ox.ac.uk/datasets/netsim/)
- VAR
- Lorenz96
Put ADNI data folder outside this repository like the structure below:
├── data
│ ├── ADNI
│ │ ├── CN
│ │ ├── ...
│ │ └── label.csv
│ ├── IRMA
│ ├── ...
│ └── Netsim
└── SpatioTemporalCausalLearning
Example: Run NOTEARS
cd SpatioTemporalCausalLearning/Baselines/NOTEARS
mkdir ECNs_results
nohup python main.py
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[NOTEARS] Zheng X, Aragam B, Ravikumar P K, et al. Dags with no tears: Continuous optimization for structure learning[J]. Advances in Neural Information Processing Systems, 2018, 31. https://github.com/xunzheng/notears
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[DAGGNN] Yu Y, Chen J, Gao T, et al. DAG-GNN: DAG structure learning with graph neural networks[C]. International Conference on Machine Learning. PMLR, 2019: 7154-7163. https://github.com/fishmoon1234/DAG-GNN
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[NRI] Kipf T, Fetaya E, Wang K C, et al. Neural relational inference for interacting systems[C]. International Conference on Machine Learning. PMLR, 2018: 2688-2697. https://github.com/ethanfetaya/NRI