Paper Contents
Abstract
Graph Theory, a fundamental area of mathematics, explores the relationships between vertices, which can represent various types of objects, connected by edges. This field has become integral to understanding and analyzing complex relationships in diverse applications, including social networks, molecular networks, disease networks, and network modeling. With the advent of artificial intelligence (AI) and its burgeoning role in everyday life, Graph Theory's applications have extended into machine learning. By leveraging graph-theoretic concepts, we can reduce the dimensionality of datasets and streamline analysis processes, thereby enhancing machine learning models. This paper explores how graph theory can be effectively applied to machine learning, demonstrating its potential to improve model performance and data interpretation.
Copyright
Copyright © 2024 Dokku Sankara Rao. This is an open access article distributed under the Creative Commons Attribution License.
