GraphUIL

Our Pytorch implementation of Graph Neural Networks for User Identity Linkage.

1. Requirements

To install requirements:

pip install -r requirements.txt

2. Repository Structure

  • data/: contains the processed data.

    • graph/: adj_s.pkl, adj_t.pkl: adjacency matrices of the source network and the target network. embeds.pkl: textual input features of two networks.
    • label/: anchor files, train_test_0.x.pkl splits the training anchors at ratios range from 0.1 to 0.9.

    The dataset Douban-Weibo is provided by the PHD student Siyuan Chen. If you use the data, please cite the following paper. More details refer to INFUNE.

    @inproceedings{chen2020infune,
       title={A Novel Framework with Information Fusion and Neighborhood Enhancement for User Identity Linkage},
       author={Chen, Siyuan and Wang, Jiahai and Du, Xin and Hu, Yanqing},
       booktitle={24th European Conference on Artificial Intelligence (ECAI)},
       pages={1754--1761},
       year={2020}
    }
    
  • logs/: saving logs

  • models/: contains loss function and metric for evaluation.

    • base.py
    • loss.py
    • netEncode.py: GNN layer.
  • UIL/GraphUIL.py: GraphUIL framework.

  • utils/: tool functions for processing data and logging.

  • config.py: hyperparameters.

  • main.py.

3. Runing

python main.py

GitHub

View Github