International Business Machines Corporation

Massachusetts Institute of Technology

Dongyu Liu

Chuang Gan

Bei Chen

Dakuo Wang

Sijia Liu

Abel Nicolas Valente

Yi Sun

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.

Interpretable visualization system for graph neural network

Growing community of inventors

Cambridge, MA, United States of America

Dongyu Liu