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Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology

Code for NeurIPS 2019 paper titled Understanding the Power of Graph Neural Networks in Learning Graph Topology

Code is written with Python 3.6.5.

Poster, Slides and video

The poster and slides can be found in doc/.

The video can be found here.

Set up

  1. git clone https://github.com/nimadehmamy/Understanding-GCN.git
  2. pip install -r requirements.txt

Source Files

  • GraphConvNet.py

Code for modular design of the graph convolutional networks

Notebooks

  • gcn-classification

Notebook for Graph Stethoscope experiments

  • gcn-moments-experiments

Notebook for validating graph moment learning theory

  • GCN-vs-FC-graph-moments

Notebook for tests comparing a fully-connected layer with GCN for learning graph moments

Citation

If you find this repository, e.g., the code and the datasets, useful in your research, please cite the following paper:

@article{dehmamy2019understanding,
  title={Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology},
  author={Dehmamy, Nima and Barab{\'a}si, Albert-L{\'a}szl{\'o} and Yu, Rose},
  journal={Advances in neural information processing systems},
  year={2019}
}

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code for the paper in NeurIPS 2019

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