This is the Pytorch implementation of IMvGCN proposed in our paper:

Zhihao Wu, Xincan Lin, Zhenghong Lin, Zhaoliang Chen, Yang Bai and Shiping Wang*, Interpretable Graph Convolutional Network for Multi-view Semi-supervised Learning, IEEE Transactions on Multimedia.

• 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 main.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.
• --Lambda: hyperparameter $\lambda$.
• --alpha: hyperparameter $\alpha$.

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

python main.py --dataset 3Sources

Please unzip the datasets folders first.

Saved in ./data/datasets/datasets.7z

Run construct_lp.py to generate laplacian matrices. Data splitting function can be found in utils.py.

Please feel free to email me for the four large datasets or any questions.

@article{10080867,
  author={Wu, Zhihao and Lin, Xincan and Lin, Zhenghong and Chen, Zhaoliang and Bai, Yang and Wang, Shiping},
  journal={IEEE Transactions on Multimedia}, 
  title={Interpretable Graph Convolutional Network for Multi-View Semi-Supervised Learning}, 
  year={2023},
  volume={25},
  number={},
  pages={8593-8606},
  doi={10.1109/TMM.2023.3260649}}