This is our PyTorch implementation for the paper:
python main.py --dataset Amazon --context_hops 3 --gpu_id 0 --gnn ApeGNN --pool sum --t_u 1 --t_i 3
The code has been tested running under Python 3.7.6. The required packages are as follows:
- pytorch == 1.7.0
- numpy == 1.18.2
- scipy == 1.4.1
- sklearn == 0.24.1
- prettytable == 2.1.0
We use three processed datasets: AMiner, Gowalla, Yelp2018 and Amazon.
| Dataset | #Users | #Items | #Interactions | Density |
|---|---|---|---|---|
| AMiner | 5,340 | 14,967 | 163,084 | 0.00204 |
| Gowalla | 29,858 | 40,981 | 1,027,370 | 0.00084 |
| Yelp2018 | 31,668 | 38,048 | 1,561,406 | 0.00130 |
| Amazon | 192,403 | 63,001 | 1,689,188 | 0.00014 |