GNN_MF_GE

Generalization Error of Graph Neural Network in the Mean-field Regime.

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Environment Setup

The codebase is implemented in Python 3.7. package versions used for development are below.

networkx                        2.6.3
numpy                           1.20.3
scipy                           1.7.1
argparse                        1.1.0
torch                           1.10.1
pyg                             2.0.3

Folder structure

• ./execution/ stores files that can be executed to generate outputs. For vast number of experiments, we use GNU parallel, which can be downloaded in command line and make it executable via:

wget http://git.savannah.gnu.org/cgit/parallel.git/plain/src/parallel
chmod 755 ./parallel

• ./joblog/ stores job logs from parallel. You might need to create it by

mkdir joblog

• ./Output/ stores raw outputs (ignored by Git) from parallel. You might need to create it by

mkdir Output

• ./data/ stores processed data sets.

• ./result_arrays/ stores results for different data sets. Each data set has a separate subfolder.

• ./exp/ stores trained models and logs.

Reproduce results

First, get into the ./execution/ folder:

cd execution

To reproduce the results to be executed on GPU-0 for machine a.

bash a0.sh

Note that if you are operating on CPU, you may delete the commands ``CUDA_VISIBLE_DEVICES=xx". You can also set you own number of parallel jobs, not necessarily following the j numbers in the .sh files, or use other GPU numbers.

Direct execution with training files

Below are various options to try:

Creating a GCN model using the mean-readout function for the ER-4 data set.

python ./run_exp.py --model_type GCN --dataset ER-4 --pooling_method mean

Creating an MPGNN model using the sum-readout function for the PROTEINS data set and run for 50 epochs.

python ./run_exp.py --model_type MPGNN --max_epochs 50 --dataset PROTEINS --pooling_method sum

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