shengyufan / ks-gnn

The implementation of KS-GNN (NeurIPS 2021).

KS-GNN

A novel method based on GNN and auto-encoder which can search keywords over an imcomplete graph (a graph with missing information).

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Dataset

The datasets except CiteSeer only include the original dataset. Please follow the setting of our work to hide keywords and edges.

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Appendix

The supplementary materials of this paper can be found via the following link: https://proceedings.neurips.cc/paper/2021/file/0d7363894acdee742caf7fe4e97c4d49-Supplemental.pdf

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Cite

If you use this code in your work, please cite our NeursIPS 2021 paper:

@inproceedings{NEURIPS2021_0d736389,
    author = {HAO, YU and Cao, Xin and Sheng, Yufan and Fang, Yixiang and Wang, Wei},
    booktitle = {Advances in Neural Information Processing Systems},
    editor = {M. Ranzato and A. Beygelzimer and Y. Dauphin and P.S. Liang and J. Wortman Vaughan},
    pages = {1700--1712},
    publisher = {Curran Associates, Inc.},
    title = {KS-GNN: Keywords Search over Incomplete Graphs via Graphs Neural Network},
    url = {https://proceedings.neurips.cc/paper/2021/file/0d7363894acdee742caf7fe4e97c4d49-Paper.pdf},
    volume = {34},
    year = {2021}
}