Study on Application of Graph Theory in Artificial Intelligence (AI)

Abstract

Mathematics play significant role in different fields/area. One of the major area of mathematics is Graph theory, In computer science, graph theory is an essential tool, particularly for artificial intelligence (AI). This study investigates the use of graph theory in several artificial intelligence (AI) fields, such as graph coloring algorithms, natural language processing (NLP), recommendation systems, and graph neural networks (GNNs). We'll show how comprehensive connection modeling and scalability in graph-based recommendation systems improve content customization, and how GNNs are excellent at managing complicated relational data in domains like traffic management, social network analysis, and drug development. We also investigate the use of graph theory to natural language processing (NLP), specifically as it relates to knowledge graphs that enhance chatbot performance, search engine accuracy, and semantic search. Furthermore, we examine how graph coloring techniques are applied in real-world situations like scheduling and register allocation, emphasizing how well they work to optimize resource usage and solve problems. This thorough analysis highlights graph theory's revolutionary influence on developing AI technologies and resolving practical issues, demonstrating its important function in both theoretical and practical contexts.