Joint Denoising and Rewiring

This repository contains the code for the paper "Joint Graph Rewiring and Feature Denoising via Spectral Alignment". The GNN code is based on the ICLR2021 paper Adaptive Universal Generalized PageRank Graph Neural Network [Paper] [Code].

Baseline Methods

Diffusion Improves Graph Learning (DIGL) [Paper] [Code]

First-order spectral rewiring (FoSR) [Paper] [Code]

Batch Ollivier-Ricci Flow (BORF) [Paper] [Code]

Approximate Message Passing - Belief Propagation (AMP-BP) [Paper] [Code]

DIGL, FoSR and BORF can be run directly from this repository. For AMP-BP, please refer to the original repository.

Requirements:

Tested with Python 3.10.14 and PyTorch 2.0.1 (Cuda 11.8).

pytorch
pytorch-geometric
numpy scipy matplotlib pyyaml

For FoSR and BORF baseline:

numba pandas networkx GraphRicciCurvature scikit-learn

Optional (if not used, use flag --no-wandb_log when running the code):

wandb

Run the Code

In all cases go to folder src

Run GCN+JDR on Cora:

python train_model.py --dataset Cora --net GCN --data_split sparse --denoise_default GCN 

Run Rewire Baselines on Cora

DIGL

python train_model.py --dataset Cora --net GCN --data_split sparse --rewire_default ppr 

FoSR

python train_model.py --dataset Cora --net GCN --data_split sparse --rewire_default fosr 

BORF

python train_model.py --dataset Cora --net GCN --data_split sparse --rewire_default borf 

Reproduce the results of the paper:

source run_csbm_exp.sh

source run_exp_table_1.sh

source run_exp_table_2.sh

How to use this ...

...with my own GNN

Add your GNN to src/GNN_models.py and adapt the argparse in src/train_model.py accordingly. Then you can test the performance of JDR with you GNN e.g. on Cora via

python train_model.py --dataset Cora --net "your GNN" --data_split sparse --denoise_default GCN 

Consider also optimizing the hyperparameters of JDR for your specific model+datasets combination as described below.

...with my own dataset

Add your dataset to the DataLoader in src/dataset_utils.pyand adapt argparse in src/train_model.py accordingly. Since no default hyperparameters exist you need to tune them yourself. Here is a suggestion on the ranges based on our findings on the other datasets:

denoise_iterations:
  distribution: int_uniform
  max: 30
  min: 1
rewired_index_A:
  distribution: int_uniform
  max: 100
  min: 1
rewired_index_X:
  distribution: int_uniform
  max: 100
  min: 1
rewired_ratio_A:
  distribution: uniform
  max: 0.5
  min: 0
rewired_ratio_X:
  distribution: uniform
  max: 0.5
  min: 0
rewired_ratio_X_non_binary:
  distribution: uniform
  max: 1
  min: 0

Datasets

Twitch-gamers

The dataset can be downloaded from Snap.

cSBM

To create a new dataset go to folder src and run for example:

python cSBM_dataset.py --phi 0.6 --name cSBM_phi_0.6 --root ../data/ --num_nodes 5000 --num_features 2000 --avg_degree 5 --epsilon 3.25

Citation

If you find our work useful, please consider citing:

@inproceedings{linkerhagner2025joint,
  title={Joint Graph Rewiring and Feature Denoising via Spectral Resonance},
  author={Jonas Linkerh{\"a}gner and Cheng Shi and Ivan Dokmani{\'c}},
  booktitle={The Thirteenth International Conference on Learning Representations},
  year={2025},
  url={https://openreview.net/forum?id=zBbZ2vdLzH}
}