Awesome

A tutorial on Graph Convolutional Neural Networks

Data

The data we use is Zachary's karate club, a standard toy social network. It is a data set consisting of:

• 34 nodes, each corresponding to members of a karate club

• 78 pairwise links that correspond to social interactions of the members outside of the club.

A conflict arose in the club which caused the club to split into several factions. Using modularity based clustering, these factions can be recovered from the graph structure alone (except for one node).

Code

• R/ contains the code necessary to produce the graphml representation of the karate club network,

• layers/graph.py contains the TensorFlow implementation of the Graph Convolutional Layer,

• utils/sparse.py contains helper functions for dealing with sparse matrices,

• examples/ contains two python scripts that demonstrate how Graph Convolutional Neural Networks perform in an unsupervised and semi-supervised manner, following the appendix of http://arxiv.org/abs/1609.02907.

Requirements

This project is built for running on an Anaconda virtual environment. I will add support for alternative setups later.

Setup

• Clone,

• If you want to use the gpu version of tensorflow, edit the environment.yml

tensorflow -> tensorflow-gpu

• Create the Anaconda virtual environment env_graph_convnet

$ conda env create

And you're ready to go!

Original implementation of Graph Convolutional Neural Networks

For the original TensorFlow implementation (in a Keras style) see https://github.com/tkipf/gcn.

References

Blog posts

• Great introductory post https://tkipf.github.io/graph-convolutional-networks/

Papers

• Defferrard, M., Bresson, X., & Vandergheynst, P. (2016). Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. Nips, (Nips), 1–14. http://arxiv.org/abs/1606.09375

• Kipf, T. N., & Welling, M. (2016). Semi-Supervised Classification with Graph Convolutional Networks, 1–14. http://arxiv.org/abs/1609.02907

• Kipf, T. N., & Welling, M. (2016). Variational Graph Auto-Encoders. Nipsw, (2), 1–3. http://arxiv.org/abs/1611.07308

• Berg, R. van den, Kipf, T. N., & Welling, M. (2017). Graph Convolutional Matrix Completion. https://arxiv.org/pdf/1706.02263.pdf

• Schlichtkrull, M., Kipf, T. N., Bloem, P., Berg, R. van den, Titov, I., & Welling, M. (2017). Modeling Relational Data with Graph Convolutional Networks, 1–12. http://arxiv.org/abs/1703.06103