Thesis Abstract

Abstract
Graph theory is a fundamental branch of mathematics that studies the properties and relationships of networks consisting of nodes and edges. In recent years, the applications of graph theory in network analysis have gained significant attention due to the increasing complexity of modern networks in various fields such as social networks, transportation systems, and communication networks. This thesis aims to explore the applications of graph theory in network analysis by investigating different aspects of network modeling and analysis techniques. The thesis begins with a comprehensive introduction to graph theory and its relevance in network analysis. The background of the study provides a detailed overview of the historical development of graph theory and its applications in various real-world scenarios. The problem statement highlights the challenges and research gaps in the current understanding of network analysis using graph theory. The objectives of the study focus on exploring the different applications of graph theory in network analysis, developing new methodologies for network modeling, and analyzing the properties of complex networks. The limitations of the study acknowledge the constraints and potential challenges in conducting research on this topic, while the scope of the study defines the boundaries and focus areas of the research. The significance of the study lies in its potential to contribute to the advancement of network analysis techniques using graph theory, leading to practical applications in diverse fields such as social sciences, computer science, and engineering. The structure of the thesis outlines the organization of the chapters and sections, providing a roadmap for the reader to navigate through the research findings. Chapter two presents a comprehensive literature review that synthesizes existing knowledge and research on the applications of graph theory in network analysis. This chapter explores various network models, algorithms, and metrics used in network analysis, providing a foundation for the subsequent research methodology. Chapter three details the research methodology used in this study, including data collection methods, network modeling techniques, and analysis procedures. The chapter outlines the steps taken to analyze network data, evaluate network properties, and draw meaningful conclusions from the findings. Chapter four presents an elaborate discussion of the research findings, including the analysis of network structures, properties, and relationships. The chapter explores how graph theory can be applied to uncover patterns, clusters, and anomalies in complex networks, leading to insights that can inform decision-making processes. In conclusion, chapter five summarizes the key findings of the study and discusses their implications for the field of network analysis using graph theory. The conclusion highlights the contributions of the research, identifies areas for future study, and emphasizes the importance of continued exploration of graph theory in network analysis. Overall, this thesis contributes to the growing body of knowledge on the applications of graph theory in network analysis, offering insights and methodologies that can be applied to analyze and understand complex networks in various domains.

Thesis Overview