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;
• and
• with said computerized user interface, obtain user input responsive to said gradient-based saliency matrix.

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;
• and
• with said computerized user interface, obtaining user input responsive to said gradient-based saliency matrix.