Implementation of ECMGD in our paper: Towards Multi-view Consistent Graph Diffusion, ACM MM 2024.

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

• Python == 3.9.12
• PyTorch == 2.2.2
• Numpy == 1.24.1
• Scikit-learn == 1.4.1
• Scipy == 1.12.0
• Texttable == 1.7.0
• Tqdm == 4.64.2

Unzip the dataset files

unzip ./data/datasets.7z

For multi-view semi-supervised classification task, run

python main.py --dataset BDGP

For heterogeneous graph node classification task, run

python main_Iso.py --dataset ACM

For incomplete multi-view semi-supervised classification task, run

python main.py --dataset BDGP --Miss_rate 0.1

Note that the default parameters may not be the best to reproduce our results in the paper.

Please unzip the datasets folders saved in ./data/HeteGraph.7z and ./data/Multi-view.7z first.

data/
│
├── Multi-view/
│   ├── BDGP.mat
│   ├── HW.mat
│   ├── MNIST10k.mat
│
└── HeteGraph/
    ├── IMDB
    ├── YELP
    └── ACM

@inproceedings{10.1145/3664647.3681258,
author = {Lu, Jielong and Wu, Zhihao and Chen, Zhaoliang and Cai, Zhiling and Wang, Shiping},
title = {Towards Multi-view Consistent Graph Diffusion},
year = {2024},
doi = {10.1145/3664647.3681258},
booktitle = {Proceedings of the 32nd ACM International Conference on Multimedia},
pages = {186–195},
}