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CayleyNets

Project developed for the Geometrical Data Analysis course of the MVA Master (ENS Paris-Saclay) by:

  • Alice Valença De Lorenci
  • Julián Avalarez de Giorgi

The goal of the project was to study the following paper: RonLevie,FedericoMonti,XavierBresson,andMichaelM.Bronstein. 2017. CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters. CoRR abs/1705.07664 (2017). arXiv:1705.07664 http://arxiv.org/abs/1705.07664

The repository contains the source code associated with the project, notably, we propose a Pytorch Geometric implementation of Cayley convolutional layers, the building block of CayleyNets.

The following package versions were used:

numpy 1.26.2
torch 2.0.1+cu117
torch_geometric 2.4.0
matplotlib 3.5.1
scipy 1.8.0
sknetwork 0.31.0
pickle 4.0

This repository is organized as follows:

  • src/CayleyNet.py: implementation of Cayley convolutional layers and CayleyNets
  • src/CayleyTransform.py: implementation of the Cayley transform
  • src/ChebNet.py: implementation of ChebNets
  • src/CommunitiesGraph.py: communities graph handler
  • src/CORA.py: CORA dataset handler
  • src/Dataset.py: generic dataset handler
  • src/utils.py: miscellaneous helper methods used to train and evaluate the models
  • CayleyTransform.ipynb: experiments around the Cayley transform
  • Experiments.ipynb: experiments with Spectral GNNs