readme.md

DiffWire: Inductive Graph Rewiring via the Lovász Bound

Accepted at the First Learning on Graphs Conference 2022

$$ \left| \frac{1}{vol(G)}CT_{uv}-\left(\frac{1}{d_u} + \frac{1}{d_v}\right)\right|\le \frac{1}{\lambda_2}\frac{2}{d_{min}} $$

@InProceedings{arnaiz2022diffwire,
  title = 	 {{DiffWire: Inductive Graph Rewiring via the Lov{\'a}sz Bound}},
  author =       {Arnaiz-Rodr{\'i}guez, Adri{\'a}n and Begga, Ahmed and Escolano, Francisco and Oliver, Nuria M},
  booktitle = 	 {Proceedings of the First Learning on Graphs Conference},
  pages = 	 {15:1--15:27},
  year = 	 {2022},
  editor = 	 {Rieck, Bastian and Pascanu, Razvan},
  volume = 	 {198},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {09--12 Dec},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v198/arnaiz-rodri-guez22a/arnaiz-rodri-guez22a.pdf},
  url = 	 {https://proceedings.mlr.press/v198/arnaiz-rodri-guez22a.html}
}

Dependencies

Conda environment

conda create --name <env> --file requirements.txt

or

conda env create -f environment_experiments.yml
conda activate DiffWire

Code organization

• datasets/: script for creating synthetic datasets. For non-synthetic ones: we use PyG in train.py
• layers/: Implementation of the proposed GAP-Layer, CT-Layer, and the baseline MinCutPool (based on his repo).
• tranforms/: Implementation og graph preprocessing baselines DIGL and SDRF, both based on the official repositories of the work.
• trained_models/: files with the weight of some trained models.
• nets.py: Implementation of GNNs used in our experiments.
• train.py: Script with inline arguments for running the experiments.

Run experiments

python train.py --dataset REDDIT-BINARY --model CTNet --cuda cuda:0
python train.py --dataset REDDIT-BINARY --model GAPNet --derivative laplacian --cuda cuda:0
python train.py --dataset REDDIT-BINARY --model GAPNet --derivative normalizeed --cuda cuda:0

experiments_all.sh list all the experiments.

Code Examples

See jupyter notebook examples at the tutorial presented at The First Learning on Graphs Conference: Graph Rewiring: From Theory to Applications in Fairness

• CT-Layer