Interpretable visualization system for graph neural network

An apparatus comprising:

• a memory;
• and
• at least one processor, coupled to said memory, and operative to:
• instantiate a computerized trained graph neural network model, a computerized graph neural network interpretation module, and a computerized user interface;
• use said computerized trained graph neural network model to classify an input instance with a predicted label;
• with said computerized graph neural network interpretation module, compute a gradient-based saliency matrix based on said input instance and said predicted label, by taking a partial derivative of class prediction with respect to an adjacency matrix of said model;
• rendering said gradient-based saliency matrix in the computerized user interface;
• with said computerized user interface, obtain user input responsive to said gradient-based saliency matrix, wherein said user input is responsive to said rendering;
• and
• determining a minimum perturbation to add to said adjacency matrix that maximizes prediction accuracy of said model, wherein:
• said rendering showing information related to said model;
• and
• said user input comprises at least one of:
• adding a missing edge based on a gradient-based saliency map-based graph explanation to improve said prediction accuracy;
• and
• at least one of derating and removing an unimportant edge based on the gradient-based saliency map-based graph explanation to improve said prediction accuracy.

A non-transitory computer readable medium comprising computer executable instructions which when executed by a computer cause the computer to perform a method of:

• instantiating a computerized trained graph neural network model, a computerized graph neural network interpretation module, and a computerized user interface;
• using said computerized trained graph neural network model to classify an input instance with a predicted label;
• with said computerized graph neural network interpretation module, computing a gradient-based saliency matrix based on said input instance and said predicted label, by taking a partial derivative of class prediction with respect to an adjacency matrix of said model;
• rendering said gradient-based saliency matrix in the computerized user interface;
• with said computerized user interface, obtaining user input responsive to said gradient-based saliency matrix, wherein said user input is responsive to said rendering;
• and
• determining a minimum perturbation to add to said adjacency matrix that maximizes prediction accuracy of said model, wherein:
• said rendering showing information related to said model;
• and
• said user input comprises at least one of:
• adding a missing edge based on a gradient-based saliency map-based graph explanation to improve said prediction accuracy;
• and
• at least one of derating and removing an unimportant edge based on the gradient-based saliency map-based graph explanation to improve said prediction accuracy.