Abstract: To enable graph partitioning using a graph convolutional neural network without preparing training data, an information processing apparatus is configured to classify a plurality of delivery destinations into a plurality of groups, and includes: a memory storing a program; and at least one processor that, by executing the program stored in the memory, is configured to: perform unsupervised learning to train a graph convolutional neural network, which is determined using an adjacency matrix indicating a connection relationship of the plurality of delivery destinations, and receives as input a feature matrix indicating a feature of the plurality of delivery destinations, the learning unit performing unsupervised learning using a first loss function defined such that the smaller a value for distance between delivery destinations belonging to a same group and the smaller a difference in features between delivery destinations belonging to a same group, the less a loss; and output information about a group to which