This DGL example implements the Heterogeneous GCN part of the model proposed in the paper Spam Review Detection with Graph Convolutional Networks.
This example was implemented by Kay Liu during his SDE intern work at the AWS Shanghai AI Lab.
- Python 3.7.10
- PyTorch 1.8.1
- dgl 0.7.0
- scikit-learn 0.23.2
The datasets used for edge classification are variants of DGL's built-in fake news datasets. The converting process from tree-structured graph to bipartite graph is shown in the figure.
NOTE: Same as the original fake news dataset, this variant is for academic use only as well, and commercial use is prohibited. The statistics are summarized as followings:
Politifact
- Nodes:
- user (u): 276,277
- news (v): 581
- Edges:
- forward: 399,016
- backward: 399,016
- Number of Classes: 2
- Node feature size: 300
- Edge feature size: 300
Gossicop
- Nodes:
- user (u): 565,660
- news (v): 10,333
- Edges:
- forward: 1,254,469
- backward: 1,254,469
- Number of Classes: 2
- Node feature size: 300
- Edge feature size: 300
In the gas folder, run
python main.py
If want to use a GPU, run
python main.py --gpu 0
If the mini-batch training is required to run on a GPU, run
python main_sampling.py --gpu 0
| Dataset | Xianyu Graph (paper reported) | Fake News Politifact | Fake News Gossipcop |
|---|---|---|---|
| F1 | 0.8143 | 0.9994 | 0.9942 |
| AUC | 0.9860 | 1.0000 | 0.9991 |
| Recall@90% precision | 0.6702 | 0.9999 | 0.9976 |