Graph Theory Applications in Data Science and Machine Learning

Abstract

Graph theory, a branch of mathematics focusing on the properties and applications of graphs, has become increasingly vital in data science and machine learning. Graphs, representing nodes and edges, model complex relationships in diverse data types, such as social networks, biological systems, and knowledge graphs. This paper explores the integration of graph theory into these fields, highlighting recent advancements and key research contributions. We discuss applications in social network analysis, biological networks, knowledge graphs, and machine learning, including Graph Neural Networks (GNNs), semi-supervised learning, and graph-based clustering. Our results demonstrate the effectiveness of graph-based methods in enhancing understanding, improving accuracy, and uncovering hidden patterns across various domains. The discussion provides insights into future research directions and potential advancements, emphasizing the broad impact of graph theory on data science and machine learning.