dgl/examples/pytorch/gas

• DGL Implementation of the GAS Paper
• • Example implementor
  • Dependencies
  • Dataset
  • How to run
  • Performance

DGL Implementation of the GAS Paper

This DGL example implements the Heterogeneous GCN part of the model proposed in the paper Spam Review Detection with Graph Convolutional Networks.

Example implementor

This example was implemented by Kay Liu during his SDE intern work at the AWS Shanghai AI Lab.

Dependencies

• Python 3.7.10
• PyTorch 1.8.1
• dgl 0.7.0
• scikit-learn 0.23.2

Dataset

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

How to run

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

Performance

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