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Towards Better Dynamic Graph Learning: New Architecture and Unified Library

This repository is built for the paper Towards Better Dynamic Graph Learning: New Architecture and Unified Library.

🔔 If you have any questions or suggestions, please feel free to let us know. You can directly email Le Yu using the email address yule@buaa.edu.cn or post an issue on this repository.

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Overview

Dynamic Graph Library (DyGLib) is an open-source toolkit with standard training pipelines, extensible coding interfaces, and comprehensive evaluating strategies, which aims to promote standard, scalable, and reproducible dynamic graph learning research. Diverse benchmark datasets and thorough baselines are involved in DyGLib.

Benchmark Datasets and Preprocessing

Fourteen datasets are used in DyGLib, including Wikipedia, Reddit, MOOC, LastFM, Myket, Enron, Social Evo., UCI, Flights, Can. Parl., US Legis., UN Trade, UN Vote, and Contact. The first five datasets are bipartite, and the others only contain nodes with a single type.

Most of the used original dynamic graph datasets come from Towards Better Evaluation for Dynamic Link Prediction, which can be downloaded here. Please download them and put them in DG_data folder. The Myket dataset comes from Effect of Choosing Loss Function when Using T-batching for Representation Learning on Dynamic Networks and can be accessed from here. The original and preprocessed files for Myket dataset are included in this repository.

We can run preprocess_data/preprocess_data.py for pre-processing the datasets. For example, to preprocess the Wikipedia dataset, we can run the following commands:

cd preprocess_data/
python preprocess_data.py  --dataset_name wikipedia

We can also run the following commands to preprocess all the original datasets at once:

cd preprocess_data/
python preprocess_all_data.py

Dynamic Graph Learning Models

Eight popular continuous-time dynamic graph learning methods are included in DyGLib, including JODIE, DyRep, TGAT, TGN, CAWN, EdgeBank, TCL, and GraphMixer. Our recent work DyGFormer is also integrated into DyGLib, which can explore the correlations of the source node and destination node by a neighbor co-occurrence encoding scheme, and effectively and efficiently benefit from longer histories via a patching technique.

Evaluation Tasks

DyGLib supports dynamic link prediction under both transductive and inductive settings with three (i.e., random, historical, and inductive) negative sampling strategies, as well as dynamic node classification.

Incorporate New Datasets or New Models

New datasets and new models are welcomed to be incorporated into DyGLib by pull requests.

Environments

PyTorch 1.8.1, numpy, pandas, tqdm, and tabulate

Executing Scripts

Scripts for Dynamic Link Prediction

Dynamic link prediction could be performed on all the thirteen datasets. If you want to load the best model configurations determined by the grid search, please set the load_best_configs argument to True.

Model Training

python train_link_prediction.py --dataset_name wikipedia --model_name DyGFormer --patch_size 2 --max_input_sequence_length 64 --num_runs 5 --gpu 0

python train_link_prediction.py --dataset_name wikipedia --model_name DyGFormer --load_best_configs --num_runs 5 --gpu 0

Model Evaluation

Three (i.e., random, historical, and inductive) negative sampling strategies can be used for model evaluation.

python evaluate_link_prediction.py --dataset_name wikipedia --model_name DyGFormer --patch_size 2 --max_input_sequence_length 64 --negative_sample_strategy random --num_runs 5 --gpu 0

python evaluate_link_prediction.py --dataset_name wikipedia --model_name DyGFormer --negative_sample_strategy random --load_best_configs --num_runs 5 --gpu 0

Scripts for Dynamic Node Classification

Dynamic node classification could be performed on Wikipedia and Reddit (the only two datasets with dynamic labels).

Model Training

python train_node_classification.py --dataset_name wikipedia --model_name DyGFormer --patch_size 2 --max_input_sequence_length 64 --num_runs 5 --gpu 0

python train_node_classification.py --dataset_name wikipedia --model_name DyGFormer --load_best_configs --num_runs 5 --gpu 0

Model Evaluation

python evaluate_node_classification.py --dataset_name wikipedia --model_name DyGFormer --patch_size 2 --max_input_sequence_length 64 --num_runs 5 --gpu 0

python evaluate_node_classification.py --dataset_name wikipedia --model_name DyGFormer --load_best_configs --num_runs 5 --gpu 0

Acknowledgments

We are grateful to the authors of TGAT, TGN, CAWN, EdgeBank, and GraphMixer for making their project codes publicly available.

Citation

Please consider citing our paper when using this project.

@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={Advances in Neural Information Processing Systems},
  year={2023}
}