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DiffPool, a differentiable graph pooling module that can generate hierarchical representations of graphs and can be combined with various graph neural network architectures in an end-to-end fashion. DiffPool learns a differentiable soft cluster assignment for nodes at each layer of a deep GNN, mapping nodes to a set of clusters, which then form the coarsened input for the next GNN layer.
More detail about DIFFPOOL can be found in:
This repository contains a implementation of DIFFPOOL based on MindSpore and GraphLearning
The experiment is based on ENZYMES.
CUDA_VISIBLE_DEVICES=0 python model_zoo/diffpool/trainval_enzymes.py --data_path {data_path}
ENZEMES dataset
Best val acc: 0.733
MindSpore Graph Learning is an efficient and easy-to-use graph learning framework, which allows researchers and developers to implement graph models according to formula easily and train efficiently.
https://gitee.com/mindspore/graphlearning
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