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.

       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)},
  • logs/: saving logs

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

    • GNN layer.
  • UIL/ GraphUIL framework.

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

  • hyperparameters.


3. Runing



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