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Cell Attention Networks

This repository contains the official code implementation for the paper Cell Attention Networks.

Cell Attention Networks propose a novel message-passing scheme for graph neural networks (GNNs) that lifts node feature vectors into a higher-dimensional space called Cellular Attention Networks. The information exchange between edges is weighted by learnable attention coefficients, which enhances the model's expressiveness and generalization.

Cell Attention Network Diagram

Table of Contents

Requirements

  • Python 3.7+
  • PyTorch 1.9+
  • torchvision 0.10+
  • torch-geometric 2.0+
  • numpy 1.20+
  • tqdm 4.62+

Installation

To install the required dependencies, run the following command:

pip install -r requirements.txt

Running experiments on TUDatasets

python ./exp/run_tu.py

Running all results on TUDatasets

sh ./exp/run_table.sh

Citation

If you find this work useful, please consider citing the paper:

@misc{giusti2022cell,
      title={Cell Attention Networks}, 
      author={Lorenzo Giusti and Claudio Battiloro and Lucia Testa and Paolo Di Lorenzo and Stefania Sardellitti and Sergio Barbarossa},
      year={2022},
      eprint={2209.08179},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

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