MAGPool: Multi-hop Attention-based Graph Pooling

Overview

This directory contains the code required to execute the MAGPool algorithm. MAGPool is a hierarchical graph pooling algorithm that utilizes Personalized PageRank and an Attention mechanism. This combination is designed to integrate multi-hop connections existing within the graph into the processes of message passing and information aggregation.

For further information you can refer to Multi-hop Attention-based Graph Pooling: A Personalized PageRank Perspective.

Requirements

MAGPool is implemented with Pytorch and Pytorch Geometric

Pytorch >= 2.0.1+cu118
Pytorch Geometric >= 2.3.1

Install the requirements:

pip install -r requirements.txt

Usage

python3 train.py

In the case of using CUDA:

CUDA_LAUNCH_BLOCKING=1 python3 main.py

NOTE: there are various arguments that you can modify, please see train.py for further details.

Cite

If you take advantage of the MAGPool, please cite the following in your manuscript:

 @INPROCEEDINGS{10454077,
  author={Haddadian, Parsa and Booryaee, Roya and Abedian, Rooholah and Moeini, Ali},
  booktitle={2024 Third International Conference on Distributed Computing and High Performance Computing (DCHPC)}, 
  title={Multi-hop Attention-based Graph Pooling: A Personalized PageRank Perspective}, 
  year={2024},
  volume={},
  number={},
  pages={1-7},
  keywords={Representation learning;High performance computing;Robustness;Graph neural networks;Task analysis;Distributed computing;Resilience;Graph Representation Learning;Graph Pooling;Attention Mechanism;Personalized PageRank},
  doi={10.1109/DCHPC60845.2024.10454077}}

Contact Us

Please do not hesitate to contact Parsa or Roya, if you had any further questions