Awesome
Understanding over-squashing and bottlenecks on graphs via curvature
Code accompanying our paper (arXiv) at ICLR 2022, recipient of an Outstanding Paper Honorable Mention.
<p align="center"> <img width="600" align="center" alt="Manifolds and graphs can both exhibit curvature" src="https://user-images.githubusercontent.com/34721006/167724021-588b9903-795e-4de8-a4ca-88d2ce7e514f.png"> </p>Setup
The script setup_env.sh assumes that you have conda installed in ~/miniconda3 (as would result from an installation like this one). If you have it installed elsewhere please change the conda activation step in the first line of setup_env.sh as needed before running
source setup_env.sh
after which you'll have a conda environment env_curvature (name can be changed in .sh file) that you can activate in future. gdl is a helper library that we used for our experiments. You also may need to change the cudatoolkit/torch versions depending on your cuda version (we used 11.1).
Usage
To calculate Balanced Forman curvature, look at
from gdl.curvature.numba import balanced_forman_curvature # if you're on CPU
from gdl.curvature.cuda import balanced_forman_curvature # if you're on GPU with CUDA
To get a torch_geometric dataset preprocessed with SDRF, look at
from gdl.data import SDRFDataset
and if you're using CUDA set
import os
os.environ["DEVICE"] = "cuda"
or otherwise set the environment variable.