wehub-resource-sync
71 lines
2.1 KiB
Markdown
71 lines
2.1 KiB
Markdown
# DGL Implementation of the GAS Paper
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This DGL example implements the Heterogeneous GCN part of the model proposed in the paper [Spam Review Detection with Graph Convolutional Networks](https://arxiv.org/abs/1908.10679).
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Example implementor
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----------------------
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This example was implemented by [Kay Liu](https://github.com/kayzliu) during his SDE intern work at the AWS Shanghai AI Lab.
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Dependencies
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----------------------
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- Python 3.7.10
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- PyTorch 1.8.1
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- dgl 0.7.0
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- scikit-learn 0.23.2
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Dataset
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The datasets used for edge classification are variants of DGL's built-in [fake news datasets](https://github.com/dmlc/dgl/blob/master/python/dgl/data/fakenews.py). The converting process from tree-structured graph to bipartite graph is shown in the figure.
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**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:
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**Politifact**
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- Nodes:
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- user (u): 276,277
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- news (v): 581
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- Edges:
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- forward: 399,016
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- backward: 399,016
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- Number of Classes: 2
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- Node feature size: 300
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- Edge feature size: 300
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**Gossicop**
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- Nodes:
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- user (u): 565,660
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- news (v): 10,333
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- Edges:
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- forward: 1,254,469
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- backward: 1,254,469
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- Number of Classes: 2
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- Node feature size: 300
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- Edge feature size: 300
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How to run
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In the gas folder, run
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```
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python main.py
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```
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If want to use a GPU, run
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```
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python main.py --gpu 0
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```
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If the mini-batch training is required to run on a GPU, run
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```
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python main_sampling.py --gpu 0
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```
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Performance
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|Dataset | Xianyu Graph (paper reported) | Fake News Politifact | Fake News Gossipcop |
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| -------------------- | ----------------- | -------------------- | ------------------- |
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| F1 | 0.8143 | 0.9994 | 0.9942 |
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| AUC | 0.9860 | 1.0000 | 0.9991 |
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| Recall@90% precision | 0.6702 | 0.9999 | 0.9976 | |