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ApeGNN: Node-Wise Adaptive Aggregation in GNNs for Recommendation (WWW'23)

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ApeGNN: Node-Wise Adaptive Aggregation in GNNs for Recommendation

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:

Environment Requirements

The code has been tested running under Python 3.9.7. The required packages are as follows:

  • pytorch == 1.9.1+cu111

Usage Example

Running one trial of Heat Kernel on AMiner:

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 &

Running one trial of PPR on AMiner:

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 &

Dataset

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

Citing

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}
}

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