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GraTO: Graph Neural Network Framework Tackling Over-smoothing with Neural Architecture Search official code repository

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#GraTO ####file structure ###draft version

├── architect.py // architecture parameter calculation and optimization

├── args.py

├── final_operation_test // opeartions used in GraTO

├── genotype.py // select operations you want to use in the searching process

├── layer.py // pairnorm code

├── LDA_loss.py // just ignore it

├── Mad_measure.py // calculation of MAD value

├── model.py // basic block code without architecture parameter

├── new_measure.py // metrics

├── new_model_search.py // basic block code with architecture parameter

├── new_train_newmeasure.py // train derived structure code

├── new_train_search_newmeasure.py // structure searching code

├── normalization.py //ignore

├── start.py // here set all parameters and train your derived model

├── utils.py

If you have any questions, please email me so I can get to know.

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GraTO: Graph Neural Network Framework Tackling Over-smoothing with Neural Architecture Search official code repository

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