Signed Bipartite Graph Neural Networks (CIKM2021)
![](https://github.com/huangjunjie-cs/SBGNN/raw/main/imgs/SBN.png)
Figure shows some common application scenarios for signed bipartite networks, including product review, bill vote, and peer review.
Some opinions can be viewed as positive relationships, such as favorable reviews on products, supporting the bill, accepting a paper, and so on. Meanwhile, some opinions are negative links that indicate negative reviews, disapproving a bill, rejecting a paper, and so forth. These scenarios can be modeled as signed bipartite networks, which include two sets of nodes (i.e., U and V) and the links with positive and negative relationships between two sets.
For bonanza, house, senate
, you can download it from this repository.
For review
dataset, you can download it in experiments-data
folder.
In order to run this code, you need to install following dependencies:
pip install torch numpy sklearn tqdm tensorboard
python sbgnn.py --lr 5e-3 --seed 222 \
--dataset_name house1to10-1 --gnn_layer 2 \
--epoch 2000 --agg AttentionAggregator
Results:
test_auc 0.8498742632577166
test_f1 0.8592910848549948
test_macro_f1 0.848896372204643
test_micro_f1 0.8496114447191806
Please cite our paper if you use this code in your own work
@inproceedings{huang2021signed,
title = {Signed Bipartite Graph Neural Networks},
author = {Huang, Junjie and Shen, Huawei and Cao, Qi and Tao, ShuChang and Cheng, Xueqi},
booktitle = {{CIKM} '21: The 30th {ACM} International Conference on Information
and Knowledge Management, Virtual Event, Queensland, Australia, November
1 - 5, 2021},
year = {2021},
pages = {740--749},
publisher = {{ACM}},
year = {2021},
url = {https://doi.org/10.1145/3459637.3482392},
doi = {10.1145/3459637.3482392},
}