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:
| Feature Name | Feature Type | Content |
|---|---|---|
| seqs_i | FixedLenFeature(int64) | sequence of a user's rated items |
| seqs_t | FixedLenFeature(float32) | sequence of timestamps in sec |
| seqs_hour | FixedLenFeature(int64) | sequence of timestamps in hour |
| seqs_day | FixedLenFeature(int64) | sequence of timestamps in day |
| seqs_weekday | FixedLenFeature(int64) | sequence of timestamps in weekday |
| seqs_month | FixedLenFeature(int64) | sequence of timestamps in month |
Below we report the HR@50, NDCG@50 and NDCG@100 results on the above provided dataset.
| Model | HR@50 | NDCG@50 | NDCG@100 |
|---|---|---|---|
| GRU4REC | 0.40903 | 0.18904 | 0.20321 |
| SASREC | 0.41802 | 0.19614 | 0.21075 |
| S2PNM | 0.41960 | 0.19536 | 0.20991 |
| BERT4REC | 0.42487 | 0.19782 | 0.21257 |
| GREC | 0.41915 | 0.19573 | 0.20974 |
| TGAT | 0.41633 | 0.19205 | 0.20679 |
| TiSASREC | 0.44583 | 0.20879 | 0.22334 |
| TimelyREC | 0.42202 | 0.19897 | 0.21315 |
| CTSMA | 0.45240 | 0.21141 | 0.22589 |
| EasyDGL (ours.) | 0.48320 | 0.23104 | 0.24476 |
conf/: configurations for loggingdata/: preprocessing scripts for data filter and splitrunme.sh: train or evaluate EasyDGL and baseline modelssrc/: codes for model definition
Supported algorithms:
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}
}