Interpretable visualization system for graph neural network

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United States of America

PATENT NO 12182698
APP PUB NO 20220101120A1
SERIAL NO

17039989

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Abstract

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Use a computerized trained graph neural network model to classify an input instance with a predicted label. With a computerized graph neural network interpretation module, compute a gradient-based saliency matrix based on the input instance and the predicted label, by taking a partial derivative of class prediction with respect to an adjacency matrix of the model. With a computerized user interface, obtain user input responsive to the gradient-based saliency matrix. Optionally, modify the trained graph neural network model based on the user input; and re-classify the input instance with a new predicted label based on the modified trained graph neural network model.

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Patent Owner(s)

Patent OwnerAddress
MASSACHUSETTS INSTITUTE OF TECHNOLOGY77 MASSACHUSETTS AVENUE CAMBRIDGE MA 02139

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Inventor(s)

Inventor Name Address # of filed Patents Total Citations
Chen, Bei Blanchardstown, IE 110 878
Gan, Chuang Cambridge, US 91 187
Liu, Dongyu Cambridge, US 8 9
Liu, Sijia Somerville, US 51 252
Sun, Yi Cambridge, US 241 3358
Valente, Abel Buenos Aires, AR 10 21
Wang, Dakuo Cambridge, US 61 151

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