PyTorch Implementation for "Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic Space (KDD2021)"
Authors: Menglin Yang; Min Zhou; Marcus Kalander; Zengfeng Huang; Irwin King
Paper: https://arxiv.org/pdf/2107.03767.pdf
Code: https://github.com/marlin-codes/HTGN
pip install -r requirements.txt
The data is cached in ./data/input/cached.
The raw datasets are in the folder ./data/input/raw
Note:
- The dataset with name
dblpis the COLAB dataset in the paper. - Link: to download more datasets
3.0 Go to the script at first
cd ./script
3.1 To quickly run the code:
python main.py --model=HTGN --dataset=enron10
3.2 To run more examples about HTGN, static method, run the following
bash example/run_htgn.sh
bash example/run_static.sh
bash example/run_grugcn.sh
3.3 To run DySAT, please refer to DySAT;
3.4 To run EvolveGCN, please refer to EvolveGCN or run the file
bash example/run_evolvegcn.sh;
3.5 To run VGRNN, please refer to VGRNN;
Note: for a unified comparison, we use inner product as decoder for the baseline models.
[1] Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic Space.
[2] Evolvegcn: Evolving graph convolutional networks for dynamic graphs.
[3] Variational graph recurrent neural networks.
[4] DySAT: Deep neural representation learning on dynamic graphs via self-attention networks
If you find this code useful, please cite the following paper:
@inproceedings{yang2021discrete,
title={Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic Space},
author={Yang, Menglin and Zhou, Min and Kalander, Marcus and Huang, Zengfeng and King, Irwin},
booktitle={Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery \& Data Mining},
pages={1975--1985},
year={2021}
}