The DyGLib_TGB repository is developed upon Dynamic Graph Library (DyGLib) and Temporal Graph Benchmark (TGB). We extend DyGLib to TGB, which additionally includes several popular dynamic graph learning methods for more exhaustive comparisons. Details of DyGLib_TGB is available here. Feedback from the community is highly welcomed for further improvements of this repository.
Eleven dynamic graph learning methods are included in DyGLib_TGB, including JODIE, DyRep, TGAT, TGN, CAWN, EdgeBank, TCL, GraphMixer, DyGFormer, Persistent Forecast, and Moving Average.
DyGLib_TGB supports both dynamic link property prediction and dynamic node property prediction tasks on TGB.
PyTorch 2.0.1, py-tgb, numpy, and tqdm.
Dynamic link property prediction could be performed on five datasets, including tgbl-wiki, tgbl-review, tgbl-coin, tgbl-comment, and tgbl-flight.
- Example of training
DyGFormerontgbl-wikidataset:
python train_link_prediction.py --dataset_name tgbl-wiki --model_name DyGFormer --patch_size 1 --max_input_sequence_length 32 --num_runs 5 --gpu 0
- If you want to use the best model configurations to train
DyGFormerontgbl-wikidataset, run
python train_link_prediction.py --dataset_name tgbl-wiki --model_name DyGFormer --load_best_configs --num_runs 5 --gpu 0
- Example of evaluating
DyGFormerontgbl-wikidataset:
python evaluate_link_prediction.py --dataset_name tgbl-wiki --model_name DyGFormer --patch_size 1 --max_input_sequence_length 32 --num_runs 5 --gpu 0
- If you want to use the best model configurations to evaluate
DyGFormerontgbl-wikidataset, run
python evaluate_link_prediction.py --dataset_name tgbl-wiki --model_name DyGFormer --load_best_configs --num_runs 5 --gpu 0
Dynamic node property prediction could be performed on three datasets, including tgbn-trade, tgbn-genre, and tgbn-reddit.
Note that if you train or evaluate on a dynamic node property prediction dataset for the first time, the data pre-processing will take a while. However, this is a one-time process.
- Example of training
DyGFormerontgbn-tradedataset:
python train_node_classification.py --dataset_name tgbn-trade --model_name DyGFormer --patch_size 1 --max_input_sequence_length 32 --num_runs 5 --gpu 0
- If you want to use the best model configurations to train
DyGFormerontgbn-tradedataset, run
python train_node_classification.py --dataset_name tgbn-trade --model_name DyGFormer --load_best_configs --num_runs 5 --gpu 0
- Example of evaluating
DyGFormerontgbn-tradedataset:
python evaluate_node_classification.py --dataset_name tgbn-trade --model_name DyGFormer --patch_size 1 --max_input_sequence_length 32 --num_runs 5 --gpu 0
- If you want to use the best model configurations to evaluate
DyGFormerontgbn-tradedataset, run
python evaluate_node_classification.py --dataset_name tgbn-trade --model_name DyGFormer --load_best_configs --num_runs 5 --gpu 0
Please consider citing the following related references when using this project.
@article{yu2023empirical,
title={An Empirical Evaluation of Temporal Graph Benchmark},
author={Yu, Le},
journal={arXiv preprint arXiv:2307.12510},
year={2023}
}
@article{yu2023towards,
title={Towards Better Dynamic Graph Learning: New Architecture and Unified Library},
author={Yu, Le and Sun, Leilei and Du, Bowen and Lv, Weifeng},
journal={arXiv preprint arXiv:2303.13047},
year={2023}
}
@article{huang2023temporal,
title={Temporal Graph Benchmark for Machine Learning on Temporal Graphs},
author={Huang, Shenyang and Poursafaei, Farimah and Danovitch, Jacob and Fey, Matthias and Hu, Weihua and Rossi, Emanuele and Leskovec, Jure and Bronstein, Michael and Rabusseau, Guillaume and Rabbany, Reihaneh},
journal={arXiv preprint arXiv:2307.01026},
year={2023}
}