==== This is the Pytorch implementation of GEGCN proposed in our paper:

• Python == 3.9.12
• PyTorch == 1.11.0
• Numpy == 1.21.5
• Scikit-learn == 1.1.0
• Scipy == 1.8.0
• Texttable == 1.6.4
• Tqdm == 4.64.0

python train_and_test.py

• --device: gpu number or 'cpu'.
• --path: path of datasets.
• --dataset: name of datasets.
• --seed: random seed.
• --fix_seed: fix the seed or not.
• --n_repeated: number of repeat times.
• --lr: learning rate.
• --weight_decay: weight decay.
• --ratio: label ratio.
• --num_epoch: number of training epochs.
• --knns: hyperparameter of the number of neighbors $k$.
• --theta: Initialize the learnable parameter $\theta_1$ and $\theta_2$.

All the configs are set as default, so you only need to set dataset. For example:

python train_and_test.py --dataset HW

@ARTICLE{10462517,
  author={Lu, Jielong and Wu, Zhihao and Zhong, Luying and Chen, Zhaoliang and Zhao, Hong and Wang, Shiping},
  journal={IEEE Transactions on Multimedia}, 
  title={Generative Essential Graph Convolutional Network for Multi-View Semi-Supervised Classification}, 
  year={2024},
  volume={26},
  number={},
  pages={7987-7999},
  doi={10.1109/TMM.2024.3374579}}