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GeniePath is a scalable approach for learning adaptive receptive fields of neural networks defined on permutation invariant graph data. GeniePath propose an adaptive path layer consists of two complementary functions designed for breadth and depth exploration respectively, where the former learns the importance of different sized neighborhoods, while the latter extracts and filters signals aggregated from neighbors of different hops away.
More detail about this model can be found in:
This repository contains a implementation of Deepwalk based on MindSpore and GraphLearning
This experiment is based on node classification datasets (Pubmed citation network dataset and Protein-Protein Interaction dataset).
CUDA_VISIBLE_DEVICES=0 python model_zoo/geniepath/trainval_pubmed.py --data_path {data_path}
CUDA_VISIBLE_DEVICES=0 python model_zoo/geniepath/trainval_ppi.py --data_path {data_path}
Pubmed citation network dataset
Test acc: 0.6490
PPI dataset
Test acc: 0.937
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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