Kay Liu
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* [Model] add model example CARE-GNN * update README * improvements based on the review feedback * fix missing item() Co-authored-by: zhjwy9343 <6593865@qq.com>
DGL Implementation of the CARE-GNN Paper
This DGL example implements the CAmouflage-REsistant GNN (CARE-GNN) model proposed in the paper Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters. The author's codes of implementation is here.
NOTE: The sampling version of this model has been modified according to the feature of the DGL's NodeDataLoader. For the formula 2 in the paper, rather than using the embedding of the last layer, this version uses the embedding of the current layer in the previous epoch to measure the similarity between center nodes and their neighbors.
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 node classification are DGL's built-in FraudDataset. The statistics are summarized as followings:
Amazon
- Nodes: 11,944
- Edges:
- U-P-U: 351,216
- U-S-U: 7,132,958
- U-V-U: 2,073,474
- Classes:
- Positive (fraudulent): 821
- Negative (benign): 7,818
- Unlabeled: 3,305
- Positive-Negative ratio: 1 : 10.5
- Node feature size: 25
YelpChi
- Nodes: 45,954
- Edges:
- R-U-R: 98,630
- R-T-R: 1,147,232
- R-S-R: 6,805,486
- Classes:
- Positive (spam): 6,677
- Negative (legitimate): 39,277
- Positive-Negative ratio: 1 : 5.9
- Node feature size: 32
How to run
To run the full graph version, in the care-gnn folder, run
python main.py
If want to use a GPU, run
python main.py --gpu 0
To train on Yelp dataset instead of Amazon, run
python main.py --dataset yelp
To run the sampling version, run
python main_sampling.py
Performance
The result reported by the paper is the best validation results within 30 epochs, while ours are testing results after the max epoch specified in the table. Early stopping with patience value of 100 is applied.
| Dataset | Amazon | Yelp | |
|---|---|---|---|
| Metric | Max Epoch | 30 / 1000 | 30 / 1000 |
| AUC | paper reported | 89.73 / - | 75.70 / - |
| DGL full graph | 89.50 / 92.35 | 69.16 / 79.91 | |
| DGL sampling | 93.27 / 92.94 | 79.38 / 80.53 | |
| Recall | paper reported | 88.48 / - | 71.92 / - |
| DGL full graph | 85.54 / 84.47 | 69.91 / 73.47 | |
| DGL sampling | 85.83 / 87.46 | 77.26 / 64.34 | |