ApeGNN develops a node-wise adaptive diffusion mechanism for information aggregation, in which each node is enabled to adaptively decide its diffusion weights based on the local structure (e.g., degree).
This is our PyTorch implementation for WWW'23 paper:
The code has been tested running under Python 3.9.7. The required packages are as follows:
- pytorch == 1.9.1+cu111
nohup python main.py --dataset aminer --gnn ApeGNN_HT --pool sum --Ks '[20, 50]' --step 1 --runs 1 --e 1e-7 --gpu_id 1 > ./logs/aminer/ApeGNN_HT.log 2>&1 &
nohup python main.py --dataset aminer --gnn ApeGNN_APPNP --pool sum --Ks '[20, 50]' --step 1 --runs 1 --e 1e-7 --gpu_id 1 > ./logs/aminer/ApeGNN_APPNP.log 2>&1 &
In the main results compared with representative models, we use six processed datasets: Ali, Amazon, AMiner, Gowalla, MovieLens, and Yelp2018.
| Dataset | #Users | #Items | #Interactions | Density |
|---|---|---|---|---|
| Ali | 106,042 | 53,591 | 907,407 | 0.016 |
| Amazon | 192,403 | 63,001 | 1,689,188 | 0.014 |
| AMiner | 5,340 | 14,967 | 163,084 | 0.204 |
| Gowalla | 29,858 | 40,981 | 1,027,370 | 0.084 |
| MovieLens | 6,040 | 3,416 | 999,611 | 4.362 |
| Yelp2018 | 31,668 | 38,048 | 1,561,406 | 0.130 |
If you find this work is helpful to your research, please consider citing our paper:
@inproceedings{zhang2023apegnn,
title={ApeGNN: Node-Wise Adaptive Aggregation in GNNs for Recommendation},
author={Zhang, Dan and Zhu, Yifan and Dong, Yuxiao and Wang, Yuandong and Wenzheng, Feng and Kharlamov, Evgeny and Tang, Jie},
booktitle={WWW'23},
year={2023}
}