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Source code for AAAI2023 "T2-GNN: Graph Neural Networks for Graphs with Incomplete Features and Structure via Teacher-Student Distillation"

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T2-GNN

Source code for AAAI2023 "T2-GNN: Graph Neural Networks for Graphs with Incomplete Features and Structure via Teacher-Student Distillation"

Dependencies

  • Python3
  • NumPy
  • SciPy
  • PyTorch
  • TensorFlow.keras

Example Usages

Before running the code, please unzip the data.zip.

  • python run.py --dataset texas --Ts 4.0 --topk 10 --lambd 0.8

Please refer to the args.py for more parameters.

Acknowledgements

The demo code is implemented based on GCN-with-Hinton-Knowledge-Distillation

Reference

If you make advantage of T2-GNN in your research, please cite the following in your manuscript:

Cuiying Huo, et al. "T2-GNN: Graph Neural Networks for Graphs with Incomplete Features and Structure via Teacher-Student Distillation." In AAAI. 2023.

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Source code for AAAI2023 "T2-GNN: Graph Neural Networks for Graphs with Incomplete Features and Structure via Teacher-Student Distillation"

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