ESNR

This repository includes the ESNR (Edge signal-to-noise ratio) part for paper "Towards Understanding and Reducing Graph Structural Noise for GNNs". ESNR is proposed as a novel metric for measuring graph structural noise level for real graph-structured datasets.

Dependencies

numpy
torch==1.13.0
sklearn
torch-scatter torch-sparse torch-cluster torch-spline-conv -f https://data.pyg.org/whl/torch-1.13.0+cu117.html
torch-geometric==2.2.0

Results

ESNR performance in synthetic contextual stochastic block model:

ESNR performance in real graph-structured datasets:

For more details, please refer to our paper: Towards Understanding and Reducing Graph Structural Noise for GNNs

@InProceedings{pmlr-v202-dong23a,
  title = 	 {Towards Understanding and Reducing Graph Structural Noise for {GNN}s},
  author =       {Dong, Mingze and Kluger, Yuval},
  booktitle = 	 {Proceedings of the 40th International Conference on Machine Learning},
  pages = 	 {8202--8226},
  year = 	 {2023},
  editor = 	 {Krause, Andreas and Brunskill, Emma and Cho, Kyunghyun and Engelhardt, Barbara and Sabato, Sivan and Scarlett, Jonathan},
  volume = 	 {202},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {23--29 Jul},
  publisher =    {PMLR}
}