README.md

Mining User-aware Multi-relations for Fake News Detection in Large Scale Online Social Networks

This repository is the official PyTorch implementation of Us-DeFake in the paper:

Mining User-aware Multi-relations for Fake News Detection in Large Scale Online Social Networks, accepted by the the 16th ACM International Conference on Web Search and Data Mining (WSDM '23) [arXiv].

Dependencies

• python >= 3.6.8
• pytorch >= 1.1.0
• cython >=0.29.2
• numpy >= 1.14.3
• scipy >= 1.1.0
• scikit-learn >= 0.19.1
• pyyaml >= 3.12
• g++ >= 5.4.0
• openmp >= 4.0

Datasets

To show the input formats of datasets, we give an example dataset "toy" in /data/ directory. The toy dataset is just used to show the input format, it's not suitable for experiments. The structure of the /data/toy/ directory should be as follows.Download Example Dataset

data/
│
└───toy/
    │   class_map.json
    │   post_graph.txt
    │   text_adj_full.npz
    │   text_feats.npy
    │   user_adj_full.npz
    │   user_feats.npy
    │   user_graph
    │   user_post_graph.txt
    └───1/
        │    text_adj_train.npz
        │    text_role.json
        │    user_adj_train.npz
        └─── user_role.json

• class_map.json: a dictionary of length N. Each key is a node index, and each value is 0 (real news) or 1 (fake news).
• post_graph.txt: propagation graph of news. It is not input to Us-DeFake, but is intended to intuitively show the contents of the text_adj_*.npz file of news.
• text_adj_full.npz: a sparse matrix in CSR format of post_graph.txt, stored as a scipy.sparse.csr_matrix. The shape is N by N. Non-zeros in the matrix correspond to all the edges in the full graph. It doesn't matter if the two nodes connected by an edge are training, validation or test nodes.
• text_feats.npy: attributes of news. They are learned by RoBERT algorithm with 768 dimensions.
• user_adj_full.npz: a sparse matrix in CSR format of user_graph.txt, stored as a scipy.sparse.csr_matrix. The shape is M by M. Non-zeros in the matrix correspond to all the edges in the full graph. It doesn't matter if the two nodes connected by an edge are training, validation or test nodes.
• user_feats.npy: attributes of users. They are representive information of users, e.g., the number of followers, the number of following, and so on.
• user_graph.txt: interaction graph of users. It is not input to Us-DeFake, but is intended to intuitively show the contents of the user_adj_*.npz file of users.
• user_post_graph.txt: posting graph of news and users.
• 1: 1st fold of k-fold cross validation.
• text_adj_train.npz: a sparse matrix in CSR format of training news, stored as a scipy.sparse.csr_matrix. The shape is also N by N. However, non-zeros in the matrix only correspond to edges connecting two training nodes. The graph sampler only picks nodes/edges from this text_adj_train, not text_adj_full. Therefore, neither the attribute information nor the structural information are revealed during training. Also, note that only aN rows and cols of text_adj_train contains non-zeros. For unweighted graph, the non-zeros are all 1.
• text_role.json: a dictionary of four keys. Key 'tr' corresponds to the list of all training node indices. Key 'va' corresponds to the list of all validation node indices. Key 'te' corresponds to the list of all test node indices. Note that in the raw data, nodes may have string-type ID. Key 'source news' corresponds to the source news. You would need to re-assign numerical ID (0 to N-1) to the nodes, so that you can index into the matrices of adj, features and class labels.
• user_adj_train.npz: a sparse matrix in CSR format of training users, stored as a scipy.sparse.csr_matrix. The shape is also M by M. However, non-zeros in the matrix only correspond to edges connecting two training nodes. The graph sampler only picks nodes/edges from this user_adj_train, not user_adj_full. Therefore, neither the attribute information nor the structural information are revealed during training. Also, note that only aN rows and cols of user_adj_train contains non-zeros. For unweighted graph, the non-zeros are all 1.
• user_role.json: a dictionary of four keys. Key 'tr' corresponds to the list of all training node indices. Key 'va' corresponds to the list of all validation node indices. Key 'te' corresponds to the list of all test node indices. Note that in the raw data, nodes may have string-type ID. You would need to re-assign numerical ID (0 to N-1) to the nodes, so that you can index into the matrices of adj, features and class labels.

Cython Implemented Parallel Graph Sampler

We have a cython module which need compilation before training can start. Compile the module by running the following from the root directory:

python graphsaint/setup.py build_ext --inplace

Training Configuration

The hyperparameters needed in training can be set via the configuration file: ./train_config/<dataset_name>.yml.

Run Training

First of all, please compile cython samplers (see above). We suggest looking through the available command line arguments defined in ./utility/globals.py.

To run the code on CPU

python -m train --data_prefix ./data/<dataset_name> --fold <fold_k> --train_config ./train_config/<dataset_name>.yml --gpu -1

To run the code on GPU

python -m train --data_prefix ./data/<dataset_name> --fold <fold_k> --train_config ./train_config/<dataset_name>.yml --gpu 0

For example, to run dataset 'toy' on CPU:

python -m train --data_prefix ./data/toy --fold 1 --train_config ./train_config/toy.yml --gpu -1

Citation & Acknowledgement

We thank Hanqing Zeng et al. proposed the GraphSAINT paper and released the code. Us-DeFake employs GraphSAINT to learn representations of news and users in large scale online social networks.

If you find this method helpful for your research, please cite our paper.

@article{su2022mining,
  title={Mining User-aware Multi-relations for Fake News Detection in Large Scale Online Social Networks},
  author={Su, Xing and Yang, Jian and Wu, Jia and Zhang, Yuchen},
  journal={arXiv preprint arXiv:2212.10778},
  year={2022}
}