/DGCN

The code of paper "A Novel Representation Learning for Dynamic Graphs Based on Graph Convolutional Networks, IEEE Transactions on Cybernetics".

Primary LanguagePython

DGCN-TCYB2022

The code related to the paper below：Chao Gao, Junyou Zhu, Fan Zhang, Zhen Wang, Xuelong Li, A novel representation learning for dynamic graphs based on graph convolutional networks, IEEE Transactions on Cybernetics, 2022, doi: 10.1109/TCYB.2022.3159661.

Run

train_dgcn.py is used to execute a full training run. After cd the file, using 'python train_dgcn.py' to run.

experiment for link prediction: please make sure isone_hot = True, islabel = False experiment for node clustering: please make sure isone_hot = False, islabel = True

Reference

If you make advantage of DGCN in your research, please cite the following in your manuscript:

@article{gao2022novel,
  title={A Novel Representation Learning for Dynamic Graphs Based on Graph Convolutional Networks},
  author={Gao, Chao and Zhu, Junyou and Zhang, Fan and Wang, Zhen and Li, Xuelong},
  journal={IEEE Transactions on Cybernetics},
  year={2022},
  doi={10.1109/TCYB.2022.3159661} 
  publisher={IEEE}
}