Awesome
Leveraging Active Learning with Auxiliary Task for Graph Anomaly Detection
Requirements
This code requires the following:
- Python==3.8
- PyTorch==2.0.1
- Numpy==1.24.4
- DGL==1.1.1+cu102
Usage
Take Cora dataset as an example:
python main.py --dataset cora --strategy_ad medoids_spec_nent_diff --device 0 --alpha 1.25 --beta 0.5 --gamma 1 --cluster_num 24 --tau 0.95 --phi 1.25
The hyperparameters for other datasets are reported as follows.
| Cora | Citeseer | BlogCatalog | Flickr | Amazon | YelpChi | |
|---|---|---|---|---|---|---|
| $\tau$ | 0.95 | 0.90 | 0.98 | 0.98 | 0.98 | 0.985 |
| $\alpha$ | 1.25 | 0.50 | 1.25 | 1.25 | 1.25 | 0.5 |
| $\beta$ | 0.50 | 2.00 | 1.00 | 0.50 | 0.8 | 1.25 |
| $\phi$ | 1.25 | 2.00 | 1.00 | 0.50 | 10 | 8.0 |
| $m$ | 24 | 24 | 18 | 27 | 10 | 20 |
The Amazon and YelpChi datasets can be found from GADBench.