CatGCN

This is our Pytorch implementation for the paper:

Weijian Chen, Fuli Feng, Qifan Wang, Xiangnan He, Chonggang Song, Guohui Ling and Yongdong Zhang. CatGCN: Graph Convolutional Networks with Categorical Node Features. In IEEE Transactions on Knowledge and Data Engineering, doi: 10.1109/TKDE.2021.3133013.

Citation

If you want to use our codes and datasets in your research, please cite:

@ARTICLE{CatGCN,  
author={Chen, Weijian and Feng, Fuli and Wang, Qifan and He, Xiangnan and Song, Chonggang and Ling, Guohui and Zhang, Yongdong},  
journal={IEEE Transactions on Knowledge and Data Engineering},   
title={CatGCN: Graph Convolutional Networks with Categorical Node Features},   
year={2021},  
volume={},  
number={},  
pages={1-1},  
doi={10.1109/TKDE.2021.3133013}}

Environment Requirement

The code has been tested running under Python 3.6.8. The required packages are as follows:

• pytorch == 1.1.0
• torch-geometric == 1.3.2
• torch-sparse == 0.4.3
• torch-cluster == 1.4.5
• torch-scatter == 1.4.0
• networkx == 2.3
• numpy == 1.16.3
• scikit-learn == 0.22.1
• texttable == 1.6.2

Training and Evaluation

The description of commands has been clearly stated in the codes (see the 'parameter_parser' function in parser.py). In addition, we provide scripts in the "sh" folder to reproduce the results in the paper, including the baseline methods.

The processed datasets can be downloaded here, and the corresponding process files are also provided.

Running commands of CatGCN are as follows:

• Tencent-age, CatGCN

CUDA_VISIBLE_DEVICES=0 python main.py \
--learning-rate 0.1 --weight-decay 1e-4 --dropout 0.3 --diag-probe 1 \
--graph-refining agc --aggr-pooling mean --grn-units none \
--bi-interaction nfm --nfm-units none \
--graph-layer pna --gnn-hops 6 --gnn-units none \
--aggr-style sum --balance-ratio 0.4  \
--edge-path './input/txn_data/user_edge.csv'  --field-path './input/txn_data/user_field.npy' --target-path './input/txn_data/user_age.csv'

• Alibaba-purchase, CatGCN

CUDA_VISIBLE_DEVICES=0 python main.py \
--learning-rate 0.1 --weight-decay 1e-5 --dropout 0.3 --diag-probe 39 \
--graph-refining agc --aggr-pooling mean --grn-units none \
--bi-interaction nfm --nfm-units 64,64,64,64 \
--graph-layer pna --gnn-hops 8 --gnn-units none \
--aggr-style sum --balance-ratio 0.9  \
--edge-path './input/ali_data/user_edge.csv'  --field-path './input/ali_data/user_field.npy' --target-path './input/ali_data/user_buy.csv'

• Alibaba-city, CatGCN

CUDA_VISIBLE_DEVICES=0 python main.py \
--learning-rate 0.1 --weight-decay 1e-5 --dropout 0.9 --diag-probe 41 \
--graph-refining agc --aggr-pooling mean --grn-units 64,64 \
--bi-interaction nfm --nfm-units none \
--graph-layer pna --gnn-hops 3 --gnn-units none \
--aggr-style sum --balance-ratio 0.3  \
--edge-path './input/ali_data/user_edge.csv'  --field-path './input/ali_data/user_field.npy' --target-path './input/ali_data/user_city.csv'

Note that the results maybe fluctuate due to the inherent randomness.

Acknowledgment

Thanks to the following implementations:

• Cluster-GCN
• PyTorch Geometric