This repository contains the implementation of the paper: TMetaNet: Topological Meta-Learning Framework for Dynamic Link Prediction.

We have referenced and adapted code from the following repositories:

1. WinGNN
   • Link: https://github.com/pursuecong/WinGNN
   • Reference: You J, Du T, Leskovec J. ROLAND: graph learning framework for dynamic graphs[C]//Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining. 2022: 2358-2366.
2. Roland
   • Link: https://github.com/snap-stanford/roland
   • Reference: Zhu Y, Cong F, Zhang D, et al. WinGNN: dynamic graph neural networks with random gradient aggregation window[C]//Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining. 2023: 3650-3662.

project-root/ ├── config/ # Configuration files ├── dataset/ # Datasets or preprocessed inputs ├── exp_scripts/ # Experiment scripts, results and logs  ├── model/ # Model architectures ├── utils/ # Utility functions ├── topo_utils/ # Utility functions of topological data analysis ├── main.py # Entry point for training/testing ├── example.yaml # Example configuration file ├── requirements.txt # Python dependencies ├── README.md # This file

• Python 3.9+

git clone https://github.com/your-username/your-project.git cd your-project pip install -r requirements.txt

cd preprocess python preprocess.py

You need to set the dataset name in preprocess.py and run the script.

cd .. python main.py --config config/config.yaml

If you use TMetaNet in your research, please cite our paper:

@article{li2025tmetanet, title={TMetaNet: Topological Meta-Learning Framework for Dynamic Link Prediction}, author={Li, Hao and Wan, Hao and Chen, Yuzhou and Ye, Dongsheng and Gel, Yulia and Jiang, Hao}, journal={arXiv preprint arXiv:2506.00453}, year={2025} }