The official implementation for "EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph Learning".

[2023.07.10] We release the early version of Pytorch-DGL codes at pre-branch.

[2023.03.16] We release the TensorFlow version of our codes for link prediction.

We use the Netflix benchmark to evaluate model performance, where the Tensorflow Record scheme is as follows:

TFRECORD Download: Google, 夸克

Below we report the HR@50, NDCG@50 and NDCG@100 results on the above provided dataset.

• conf/: configurations for logging
• data/: preprocessing scripts for data filter and split
• runme.sh: train or evaluate EasyDGL and baseline models
• src/: codes for model definition

Download our data to $DATA_HOME directory, then Reproduce above results on Netflix benchmark:

bash runme.sh ${$DATA_HOME}

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}
}