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Personalized propagation of neural predictions (PPNP), and its fast approximation (APPNP), use the relationship between graph convolutional networks (GCN) and PageRank to derive an improved propagation scheme based on personalized PageRank. It leverages a large, adjustable neighborhood for classification and can be easily combined with any neural network.
More detail about APPNP can be found in:
This repository contains a implementation of APPNP based on MindSpore and GraphLearning
The experiment is based on Cora-ML, which was extracted in "Deep gaussian embedding of attributed graphs: Unsupervised inductive learning via ranking." ICLR 2018
CUDA_VISIBLE_DEVICES=0 python model_zoo/appnp/trainval_cora.py --data_path {data_path}
Cora dataset
Test acc: 0.8350
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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