Deep Temporal Graph Clustering

This is a comprehensive repository that brings together our work on deep temporal graph clustering, including a series of related papers, open source datasets, and implementations of the TGC base code.

If you find any problems, feel free to contact us: mengliuedu@163.com.

Related Papers

[1] ICLR 2024: Deep Temporal Graph Clustering [paper][code]

Authors: Meng Liu, Yue Liu, Ke Liang, Wenxuan Tu, Siwei Wang, Sihang Zhou, Xinwang Liu.

Best Paper Award of 2024 China Computational Power Conference.

Youth Oustanding Paper Award Shortlist of 2025 World Artificial Intelligence Conference.

Excellent Poster Award of 2024 World Young Scientist Summit.

[2] TPAMI 2025: Deep Temporal Graph Clustering: A Comprehensive benchmark and Datasets [paper][code]

Authors: Meng Liu, Ke Liang, Siwei Wang, Xingchen Hu, Sihang Zhou, Xinwang Liu.

Excellent Paper Award of 2025 Guangdong-HongKong-Macao Conference on Image and Graphics.

[3] TNNLS 2025: Multiview Temporal Graph Clustering [paper][code]

Authors: Meng Liu, Ke Liang, Hao Yu, Lingyuan Meng, Siwei Wang, Sihang Zhou, Xinwang Liu.

Code of TGC

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

Prepare

To run the code, you need prepare datasets and pretrain embeddings:

For Datasets

You can download the datasets from Data4TGC and create “data” folder in the same directory as the “emb” and “framework” folders.

For Pre-Training

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

Note that these embeddings are used for TGC training, while the features in the dataset are used for training by any other method.

That is, the pre-training of node2vec is only part of the TGC.

For Training

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

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

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, run the main.py in the ./framework folder, all parameter settings have default values, you can adjust them in main.py.

Test

For test, you have two ways:

(1) In the training process, we evaluate the clustering performance for each epoch.

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

Note that the node embeddings in the ./emb./patent/patent_TGC_200.emb folder are just placeholders, you need to run the main code to generate them.

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

@ARTICLE{MVTGC_ML_TNNLS,
  author={Liu, Meng and Liang, Ke and Yu, Hao and Meng, Lingyuan and Wang, Siwei and Zhou, Sihang and Liu, Xinwang},
  journal={IEEE Transactions on Neural Networks and Learning Systems}, 
  title={Multiview Temporal Graph Clustering}, 
  year={2025},
  pages={1-14}
}