Breaking the Expressive Bottleneck of Graph Neural Networks

This is the code of the paper Breaking the Expressive Bottleneck of Graph Neural Networks.

Table of Contents

• Requirements
• Run Experiments
  • ogbg-ppa
  • ogbg-code
  • ogbg-molhiv & ogbg-molpcba
  • QM9
  • TU

Requirements

The code is built upon the PyTorch Geometric, which has been tested running under Python 3.6.9.

The following packages need to be installed manually:

• torch==1.5.1
• torch-geometric==1.5.0

The following packages can be installed by running

python3 -m pip install -r requirements.txt

• ogb==1.2.1
• numpy
• easydict
• tensorboardX

Run Experiments

You can run experiments on multiple datasets as follows.

ogbg-ppa

To run experiments on ogbg-ppa, change directory to ogbg/ppa:

cd ogbg/ppa

You can set hyper-parameters in ogbg-ppa.json.

You can change CUDA_VISIBLE_DEVICES and output directory in run_script.sh.

Then, run the following script:

./run_script.sh

ogbg-code

To run experiments on ogbg-code, change directory to ogbg/code:

cd ogbg/code

You can set hyper-parameters in ogbg-code.json.

You can change CUDA_VISIBLE_DEVICES and output directory in run_script.sh.

Then, run the following script:

./run_script.sh

ogbg-molhiv & ogbg-molpcba

To run experiments on ogbg-mol*, change directory to ogbg/mol:

cd ogbg/mol

You can set dataset name and hyper-parameters in ogbg-mol.json. Dataset name should be either ogbg-molhiv or ogbg-molpcba.

You can change CUDA_VISIBLE_DEVICES and output directory in run_script.sh.

Then, run the following script:

./run_script.sh

QM9

To run experiments on QM9, change directory to qm9:

cd qm9

You can set hyper-parameters in QM9.json.

You can change CUDA_VISIBLE_DEVICES and output directory in run_script.sh.

Then, run the following script:

./run_script.sh

TU

To run experiments on TU, change directory to tu:

cd tu

There are several datesets in TU. You can set dataset name in run_script.sh and set hyper-parameters in configs/<dataset>.json.

You can change CUDA_VISIBLE_DEVICES and output directory in run_script.sh.

Then, run the following script:

./run_script.sh