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https://github.com/gravins/tg-ode
Official reference implementation of our paper "Temporal Graph ODEs for Irregularly-Sampled Time Series" accepted at IJCAI 24
https://github.com/gravins/tg-ode
dynamic-graphs graph-neural-networks graphneuralnetwork ordinary-differential-equations temporal-graphs
Last synced: about 2 months ago
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Official reference implementation of our paper "Temporal Graph ODEs for Irregularly-Sampled Time Series" accepted at IJCAI 24
- Host: GitHub
- URL: https://github.com/gravins/tg-ode
- Owner: gravins
- Created: 2024-04-23T09:26:02.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-09-10T10:05:21.000Z (4 months ago)
- Last Synced: 2024-09-10T11:30:51.853Z (4 months ago)
- Topics: dynamic-graphs, graph-neural-networks, graphneuralnetwork, ordinary-differential-equations, temporal-graphs
- Language: Python
- Homepage:
- Size: 41.4 MB
- Stars: 10
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# TG-ODE
This repository provides the official reference implementation of our paper [**_"Temporal Graph ODEs for Irregularly-Sampled Time Series"_**](https://www.ijcai.org/proceedings/2024/445) accepted at the International Joint Conference on Artificial Intelligence (IJCAI) 2024.
Please consider citing us
@inproceedings{gravina2024tgode,
title = {Temporal Graph ODEs for Irregularly-Sampled Time Series},
author = {Gravina, Alessio and Zambon, Daniele and Bacciu, Davide and Alippi, Cesare},
booktitle = {Proceedings of the Thirty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-24}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Kate Larson},
pages = {4025--4034},
year = {2024},
month = {8},
note = {Main Track},
doi = {10.24963/ijcai.2024/445},
url = {https://doi.org/10.24963/ijcai.2024/445},
}
## Requirements
_Note: we assume Miniconda/Anaconda is installed, otherwise see this [link](https://docs.conda.io/projects/conda/en/latest/user-guide/install/download.html) for correct installation. The proper Python version is installed during the first step of the following procedure._
1. Install the required packages and create the environment
- ``` conda env create -f env.yml ```
2. Activate the environment
- ``` conda activate tgode ```
## How to reproduce our experiments
First, extract the preprocessed data through the command: ```tar -xvf RESULTS.tar.xz```
Then:
```
export data="" # choose one from ['metrla', 'pems03', 'pems04', 'pems07', 'pems08', 'montevideo', 'heat', 'pow_2_heat', 'pow_5_heat', 'tanh_heat', 'expand_heat', 'reduce_heat', 'gaussian_noise_heat']
export model="" # choose one from ['DCRNN', 'GCRN_LSTM', 'GCRN_GRU', 'TGCN', 'A3TGCN', 'NODE', 'GDE', 'TGODE']
export NUM_CPUS=90 # number of available cpus for the entire experiment
export PERC_GPUS=0.0 # percentage of gpus for one configuration
export CUDA_VISIBLE_DEVICES="" # list of cuda visible devices
```
- Single-spike heat diffusion
```
export batch=16
export dir=RESULTS/single_spike/$data/
nohup python3 -u main.py --singlespike --data $data --model $model --batch $batch --savedir $dir --x_scaler StandardScaler >$dir/out_$model_$data 2>$dir/err_$model_$data
```
- Multi-spike heat diffusion
```
export batch=16
export dir=RESULTS/multi_spike/$data/
nohup python3 -u main.py --data $data --model $model --batch $batch --savedir $dir --x_scaler StandardScaler >$dir/out_$model_$data 2>$dir/err_$model_$data
```
- Traffic forecasting
```
export batch=1
export dir=RESULTS/$data/
nohup python3 -u main.py --data $data --model $model --batch $batch --savedir $dir --x_scaler StandardScaler >$dir/out_$model_$data 2>$dir/err_$model_$data
```