AMPG

The official implementation of the paper "Adaptive Message Passing Mechanism for Graph Neural Networks".

The overall Architecture of AMPG

Dependencies

This is the list of the package versions required for our experiments.

python==3.8.18
pytorch==1.13.1
torch_geometric==2.4.0
torch_sparse==0.6.15
torch_scatter==2.1.0
torch_cluster==1.6.0
torch_spline_conv==1.2.1
wandb==0.15.12

Run

We manage our experiments with wandb, to reproduce the results we reported in our paper, please follow these steps:

• Set up the environment variables. Below 2 environment variables $YOUR_WANDB_ENTITY$ and $YOUR_WANDB_PROJECT$ are your wandb username and the name of the project.

  export WANDB_entity=$YOUR_WANDB_ENTITY$
  export WANDB_project=$YOUR_WANDB_PROJECT$

• Choose best hyper-parameters you want to run with, and create wandb sweep with that file.

  We record the best hyper-parameters in folder best_params, you can index corresponding file by name.

  python sweep.py --sweep_file=best_params/Squirrel_AMPG.yaml

• You will get an sweep ID $SWEEP_ID$ and sweep URL $SWEEP_URL$ from last step, like:

  Create sweep with ID: $SWEEP_ID$
  Sweep URL: $SWEEP_URL$

  then run below command will start runs with GPU. Parameter $INDEX_GPU$:$PARALLEL_RUNS$ indicate we will run $PARALLEL_RUNS$ runs in parallel with GPU $INDEX_GPU$.

  python agents.py --sweep_id=$SWEEP_ID$ --gpu_allocate=$INDEX_GPU$:$PARALLEL_RUNS$

• You can check the results in $SWEEP_URL$, a website hosted on wandb.ai.