Awesome
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
.
Key Papers
[1] ICLR 2024: Deep Temporal Graph Clustering
Authors: Meng Liu, Yue Liu, Ke Liang, Wenxuan Tu, Siwei Wang, Sihang Zhou, Xinwang Liu
Link: https://arxiv.org/abs/2305.10738
Award
Best Paper Award of 2024 China Computational Power Conference.
Excellent Poster Award of 2024 World Young Scientist Summit..
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.
@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}
}