BenchTGC

Deep Temporal Graph Clustering: A Comprehensive benchmark and Datasets

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2025.

This is the PyTorch version of BenchTGC. We want to provide you with as much usable code as possible.

If you find any problems, feel free to contact us: [email protected].

BenchTGC Datasets

You can download the datasets from Data4TGC and create "data" folder in the same directory as the "emb" and "code" folders.

BenchTGC Framework

Prepare

To run the code, you need download datasets first.

Pre-Training

In ./code/pretrain/, you need run the pretrain.py to generate pretrain embeddings.

Note that these embeddings are pre-trained embeddings, while the features in the dataset are positional encoding embeddings.

Training

You need create a folder for each dataset in ./emb/ to store generated node embeddings.

For example, after training with School dataset, the node embeddings will be stored in ./emb/school/

Run

For each dataset, create a folder in emb folder with its corresponding name to store node embeddings, i.e., for arXivAI dataset, create ./emb/arXivAI.

For training, we give 5 improved methods, you can run them respectively.

All parameter settings have default values, you can adjust them.

Test

For test, you have two ways:

(1) In the training process, we evaluate the clustering performance for each epoch. This evaluation is used for common-scale datasets, i.e., DBLP, Brain, Patent, and School.

(2) You can also run the clustering.py in the ./code folder.

Note that the node embeddings in the ./emb/school/school_ITREND.emb folder are just placeholders, you need to run the main code to generate them.

Note that the evaluation of the School dataset during training is not ideal, so we encourage the use of trained embeddings for clustering.

Cite us

If you feel our work has been helpful, thank you for the citation.

@ARTICLE{BenchTGC_ML_TPAMI,
  author={Liu, Meng and Liang, Ke and Wang, Siwei and Hu, Xingchen and Zhou, Sihang and Liu, Xinwang},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={Deep Temporal Graph Clustering: A Comprehensive benchmark and Datasets}, 
  year={2025}
}

@inproceedings{TGC_ML_ICLR,
  title={Deep Temporal Graph Clustering},
  author={Liu, Meng and Liu, Yue and Liang, Ke and Tu, Wenxuan and Wang, Siwei and Zhou, Sihang and Liu, Xinwang},
  booktitle={The 12th International Conference on Learning Representations},
  year={2024}
}