sinet's Introduction

SINET

This repository reproduces the experimental results of a paper "Causal Effect Estimation on Hierarchical Spatial Graph Data" presented in KDD 2023.
https://dl.acm.org/doi/10.1145/3580305.3599269
An example of hierarchical spatial graph data with global and local graphs containing covariates (blue), intervention (green).

An example of network structures of SINet

Requirements

python 3
To install requirements:

conda env create -f environment.yml
conda activate sinet_env

Directory structure

.
├── README.md
├── config
│   └── GWN-GWN-MLP
│       ├── make_script.py
│       ├── run_script.sh
│       ├── script
│       └── template.yml
├── data
│   ├── experiment
│   ├── sample
│   │   ├──xz <- download sample.zip and unzip it
│   │   └──y  <- download sample.zip and unzip it
│   ├── source
│   └── y_scaler.pkl
├── demo_sinet.py <- run this!
├── environment.yml
├── model
│   ├── sinet.py
│   └── template.py
├── out
└── util
    ├── smoothmax.py
    ├── util_dataloader.py
    ├── util_globalgraph.py
    ├── util_seatgraph.py
    └── util_stgraph.py

Preprocessing

The simulation data can be download from here and should be set in the folder ./data/.

Main analysis

see demo_sinet.py for run a demo.
see ./config/run_script.sh for commands for running scripts.
Further details are documented within the code.

Citation

If you find this repository, e.g., the code and the datasets, useful in your research, please cite the following paper:

@inproceedings{takeuchi2023sinet,
  title={Causal Effect Estimation on Hierarchical Spatial Graph Data},
  author={Takeuchi, Koh and Nishida, Ryo and Kashima, Hisashi and Onishi, Masaki},
  booktitle={the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '23)},
  year={2023}
}

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