/DyGLib_TGB

An Empirical Evaluation of Temporal Graph Benchmark

Primary LanguagePythonMIT LicenseMIT

An Empirical Evaluation of Temporal Graph Benchmark

Overview

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.

Dynamic Graph Learning Models

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.

Evaluation Tasks

DyGLib_TGB supports both dynamic link property prediction and dynamic node property prediction tasks on TGB.

Environments

PyTorch 2.0.1, py-tgb, numpy, and tqdm.

Executing Scripts

Scripts for Dynamic Link Property Prediction

Dynamic link property prediction could be performed on five datasets, including tgbl-wiki, tgbl-review, tgbl-coin, tgbl-comment, and tgbl-flight.

Model Training

  • Example of training DyGFormer on tgbl-wiki dataset:
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 DyGFormer on tgbl-wiki dataset, run
python train_link_prediction.py --dataset_name tgbl-wiki --model_name DyGFormer --load_best_configs --num_runs 5 --gpu 0

Model Evaluation

  • Example of evaluating DyGFormer on tgbl-wiki dataset:
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 DyGFormer on tgbl-wiki dataset, run
python evaluate_link_prediction.py --dataset_name tgbl-wiki --model_name DyGFormer --load_best_configs --num_runs 5 --gpu 0

Scripts for Dynamic Node Property Prediction

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.

Model Training

  • Example of training DyGFormer on tgbn-trade dataset:
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 DyGFormer on tgbn-trade dataset, run
python train_node_classification.py --dataset_name tgbn-trade --model_name DyGFormer --load_best_configs --num_runs 5 --gpu 0

Model Evaluation

  • Example of evaluating DyGFormer on tgbn-trade dataset:
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 DyGFormer on tgbn-trade dataset, run
python evaluate_node_classification.py --dataset_name tgbn-trade --model_name DyGFormer --load_best_configs --num_runs 5 --gpu 0

Citations

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}
}