The official implementation for "EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph Learning".
[2023.07.28] We release the pre-version of Pytorch-DGL for traffic forecasting.
[2023.07.10] We release the pre-version of Pytorch-DGL for link prediction.
Below we report the HR@50, NDCG@50 and NDCG@100 results on the above provided dataset.
Model | HR@50 | NDCG@50 | NDCG@100 |
---|---|---|---|
EasyDGL (Tensorflow) | 0.48320 | 0.23104 | 0.24476 |
EasyDGL (Torch) | 0.48252 | 0.23055 | 0.24378 |
Below we summarize the performance with regards to different metrics and horizons on the METR-LA dataset (see DCRNN).
H=3 | H=6 | H=12 | ||
---|---|---|---|---|
DCRNN | MAE | 2.77 | 3.15 | 3.60 |
RMSE | 5.38 | 6.45 | 7.60 | |
MAPE(%) | 7.30 | 8.80 | 10.50 | |
AGCRN | MAE | 2.87 | 3.23 | 3.62 |
RMSE | 5.58 | 6.58 | 7.51 | |
MAPE(%) | 7.70 | 9.00 | 10.38 | |
EasyDGL | MAE | 2.74 | 3.10 | 3.55 |
RMSE | 5.21 | 6.17 | 7.17 | |
MAPE(%) | 6.96 | 8.19 | 9.81 |
conf/
: configurations for loggingdata/
: dataset for model training and evaluationrunme.sh
: train or evaluate EasyDGL and baseline modelssrc/
: codes for model definition
Here are commands for training the model for link prediction and traffic forecasting respectively.
python src/demo_recsys.py --config conf/model/Netflix/EasyDGL.yaml
python src/demo_traffic.py --config conf/model/METR-LA/EasyDGL.yaml
If you find our codes useful, please consider citing our work
@inproceedings{chen2021learning,
title={Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential Recommendation},
author={Chen, Chao and Geng, Haoyu and Yang, Nianzu and Yan, Junchi and Xue, Daiyue and Yu, Jianping and Yang, Xiaokang},
booktitle={Proceedings of the International Conference on Machine Learning (ICML '21)},
pages={1606--1616},
year={2021},
organization={PMLR}
}
@article{chen2023easydgl,
title={EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph Learning},
author={Chen, Chao and Geng, Haoyu and Yang, Nianzu and Yang, Xiaokang and Yan, Junchi},
journal={arXiv preprint arXiv:2303.12341},
year={2023}
}