Feature weaving network for estimation of task solutions on input graphs

Abstract

A purpose of the present invention is to improve precision when estimating the solution to a task expressed in a graph. A model generation device according to one embodiment of the present invention performs machine learning of an estimation module provided with a feature-incorporated network and an estimator. The feature-incorporated network is provided with a plurality of feature-incorporated layers. An encoder for each feature-incorporated layer is configured so as to incorporate the feature amount of the outflow edge and inflow edge of each vertex after reflecting the relationship with other vertices. Moreover, the encoder for each feature-incorporated layer is configured so as to derive the relative feature amount for each edge based on the feature amount for all of the edges inputted for a target vertex, without calculating the weighted sum of the features of the edges adjacent to each vertex.