Information processing device, information processing method, and storage medium

An information processing method comprising:

• by a computer,
• acquiring graph structure data that includes a plurality of real nodes and one or more real edges connecting two of the plurality of real nodes;
• and
• executing processing of setting a neural network that propagates a feature amount of each of a plurality of assumed nodes, the plurality of assumed nodes including the plurality of real nodes and the one or more real edges, that are distinguishable from each other, and propagating a feature amount of a k-1th layer of each of the plurality of assumed nodes at least to a feature amount of a kth layer of another assumed node in a connection relationship with each of the assumed nodes in the neural network on the basis of the acquired graph structure data, k being a natural number equal to or more than 1,
• wherein the information processing method comprises:
• setting at least part of the coefficient when the feature amount is propagated using a rule based on a graph attention network;
• and
• setting a propagation matrix to be applied to each of the feature amount of an assumed node of a propagation source and the feature amount of an assumed node of a propagation destination according to the type of the assumed node of a propagation source.

A non-transitory computer-readable storage medium storing program causing a computer to perform:

• acquiring graph structure data that includes a plurality of real nodes and one or more real edges connecting two of the plurality of real nodes;
• and
• executing processing of setting a neural network that propagates a feature amount of each of a plurality of assumed nodes, the plurality of assumed nodes including the plurality of real nodes and the one or more real edges, that are distinguishable from each other, and propagating a feature amount of a k-1th layer of each of the plurality of assumed nodes at least to a feature amount of a kth layer of another assumed node in a connection relationship with each of the assumed nodes in the neural network on the basis of the acquired graph structure data, k being a natural number equal to or more than 1,
• wherein the program causes the computer to perform:
• setting at least part of the coefficient when the feature amount is propagated using a rule based on a graph attention network;
• and
• setting a propagation matrix to be applied to each of the feature amount of an assumed node of a propagation source and the feature amount of an assumed node of a propagation destination according to the type of the assumed node of a propagation source.