Code for KDD 24 paper Topology-monitorable Contrastive Learning on Dynamic Graphs.

This code is implemented based on InstantGNN and GGD.

Requirements

• CUDA 10.1
• python 3.8.5
• pytorch 1.7.1
• GCC 5.4.0
• cython 0.29.21
• eigency 1.77
• numpy 1.18.1
• torch-geometric 1.6.3
• tqdm 4.56.0
• ogb 1.2.4
• [eigen 3.3.9] (https://gitlab.com/libeigen/eigen.git)

Datasets

OGB Datasets can be downloaded from here. The website 'Open Graph Benchmark' provides an automatic method to download and convert the three datasets. So you can straightly run 'python convert_ogb.py' instead of downloading these datasets manually. We drop several edges to simulate the graphs' evolving nature. In the folder './convert/', we provide the codes to convert the three datasets.

Compilation

Cython needs to be compiled before running, run this command:

python setup.py build_ext --inplace

Running the code

• On OGB and Patent datasets

./all.sh

Reference

If you find this repository useful in your research, please consider citing the following paper:

@inproceedings{zhu2024topology,
  title={Topology-monitorable Contrastive Learning on Dynamic Graphs},
  author={Zhu, Zulun and Wang, Kai and Liu, Haoyu and Li, Jintang and Luo, Siqiang},
  booktitle={Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  pages={4700--4711},
  year={2024}
}