readme_sigat.md

Signed Graph Attention Networks

Update: We optimized the architecture of the model and proposed a new model (SDGNN), and related research results are published on AAAI2021. more detail

Overview

This paper is accepted at ICANN2019.

We provide a Pytorch implementation of Signed Graph Attention Networks, which incorporates graph motifs into GAT to capture two well-known theories in signed network research, i.e., balance theory and status theory.

Requirements

The script has been tested running under Python 3.6.3, with the following packages installed (along with their dependencies):

pip install -r requirements.txt

Parameters

parser.add_argument('--devices', type=str, default='cpu', help='Devices')
parser.add_argument('--seed', type=int, default=13, help='Random seed.')
parser.add_argument('--epochs', type=int, default=100, help='Number of epochs to train.')
parser.add_argument('--lr', type=float, default=0.0005, help='Initial learning rate.')
parser.add_argument('--weight_decay', type=float, default=0.0001, help='Weight decay (L2 loss on parameters).')
parser.add_argument('--dataset', default='bitcoin_alpha', help='Dataset')
parser.add_argument('--dim', type=int, default=20, help='Embedding Dimension')
parser.add_argument('--fea_dim', type=int, default=20, help='Feature Embedding Dimension')
parser.add_argument('--batch_size', type=int, default=500, help='Batch Size')
parser.add_argument('--dropout', type=float, default=0.0, help='Dropout k')
parser.add_argument('--k', default=1, help='Folder k')

Run Example

Firstly, run python sigat.py get node embeddings, then run python logistic_function.py to get results.

pos_ratio: 0.9394377842083506
accuracy: 0.944605208763952
f1_score: 0.971001947630383
macro f1_score: 0.6767452134465279
micro f1_score: 0.944605208763952
auc score: 0.8886568520333262

Bibtex

Please cite our paper if you use this code in your own work:

@inproceedings{huang2019signed,
  title={Signed graph attention networks},
  author={Huang, Junjie and Shen, Huawei and Hou, Liang and Cheng, Xueqi},
  booktitle={International Conference on Artificial Neural Networks},
  pages={566--577},
  year={2019},
  organization={Springer}
}

Acknowledgement

Some codes are adapted from paper and pyGAT