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SLiCE

Self-Supervised Learning of Contextual Embeddings for Link Prediction in Heterogeneous Networks

Dataset details:

Install instructions:

  • Dependencies: Python 3.6, PyTorch 1.4.0 w/ CUDA 9.2, Pytorch Geometric
  • The specific Pytorch Geometric wheels we use are included in the repo for convenience in the 'wheels' directory
conda create -n slice python=3.6
conda activate slice
pip install -r requirements.txt

Training:

python main.py \
    --data_name 'amazon_s' \
    --data_path 'data' \
    --outdir 'output/amazon_s' \
    --pretrained_embeddings 'data/amazon_s/amazon_s.emd' \
    --n_epochs 10 \
    --n_layers 4 \
    --n_heads 4 \
    --gcn_option 'no_gcn' \
    --node_edge_composition_func 'mult' \
    --ft_input_option 'last4_cat' \
    --path_option 'shortest' \
    --ft_n_epochs 10 \
    --num_walks_per_node 1 \
    --max_length 6 \
    --walk_type 'dfs' \
    --is_pre_trained

Citation:

Please cite the following paper if you use this code in your work.

@inproceedings{wang2020self,
  title={Self-Supervised Learning of Contextual Embeddings for Link Prediction in Heterogeneous Networks},
  author={Wang, Ping and Agarwal, Khushbu and Ham, Colby and Choudhury, Sutanay and Reddy, Chandan K},
  booktitle={Proceedings of The Web Conference 2021},
  year={2021}
}

Notice

This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor Battelle, nor any of their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.

    PACIFIC NORTHWEST NATIONAL LABORATORY
    operated by
    BATTELLE
    for the
    UNITED STATES DEPARTMENT OF ENERGY
    under Contract DE-AC05-76RL01830
   

License

Released under the 3-Clause BSD license (see License.md)

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