Systems and methods for using graph neural networks for detecting competitors

Abstract

In some aspects, the techniques described herein relate to a method including: providing a graph neural network; and configuring the graph neural network to predict a competitor, the method comprising: receiving at least one dataset of nodes of supply chain companies, competitor companies, and customers and associated attached nodes' attributes; applying a first-order proximity to denote a local connection structure of some supply chain companies, competitor companies, and customers; applying a Laplacian Eigenmap to the first-order proximity to identify at least two positive pairs and at least two negative pairs; applying a pairwise ranking loss function that reduces the distance between the at least two positive pairs and increasing the distance between the at least two negative pairs; and based on an input identification of one company, ranking competitor companies of the company based on their Euclidean distances in the graph.