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GAMC: An Unsupervised Method for Fake News Detection using Graph Autoencoder with Masking

GAMC is an unsupervised fake news detection technique using the graph autoencoder with masking and contrastive learning. The code related to the paper below：

Shu Yin, Peican Zhu, Lianwei Wu, Chao Gao, Zhen Wang, GAMC: An Unsupervised Method for Fake News Detection using Graph Autoencoder with Masking, Proceedings of the AAAI conference on artificial intelligence, 2024, 38(1): 347-355.

Dependencies

• Python >= 3.7
• Pytorch >= 1.9.0
• dgl >= 0.7.2
• pyyaml == 5.4.1

Datasets

Due to file size upload limitations, datasets can be found at https://drive.google.com/drive/folders/1OslTX91kLEYIi2WBnwuFtXsVz5SS_XeR?usp=sharing.

Start

For the program start, you could run the script:

python main_graph.py --dataset DATASETNAME --use_cfg

Reference

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

@inproceedings{yin2024gamc,
  title={Gamc: an unsupervised method for fake news detection using graph autoencoder with masking},
  author={Yin, Shu and Zhu, Peican and Wu, Lianwei and Gao, Chao and Wang, Zhen},
  booktitle={Proceedings of the AAAI conference on artificial intelligence},
  volume={38},
  number={1},
  pages={347--355},
  year={2024}
}