Invariant and Equivariant Graph Networks
A PyTorch implementation of The ICLR 2019 paper “Invariant and Equivariant Graph Networks” by Haggai Maron, Heli Ben-Hamu, Nadav Shamir and Yaron Lipman
https://openreview.net/forum?id=Syx72jC9tm. The official TensorFlow implementation is at https://github.com/Haggaim/InvariantGraphNetworks
Data
Data should be downloaded from: https://www.dropbox.com/s/vjd6wy5nemg2gh6/benchmark_graphs.zip?dl=0.
Run the following commands in order to unzip the data and put its proper path.
mkdir data
unzip benchmark_graphs.zip -d data
Prerequisites
Python3
PyTorch 1.5.0
Additional modules: numpy, pandas, matplotlib
TensorFlow is not neccessary except if you want to run the tests (comparisons) between the PyTorch and TensorFlow versions.
Running the tests
Run the tests comparing between PyTorch and TensorFlow versions’ tensor contractions. All tensor contractions are implemented 1-to-1. The two versions have identical tensor