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Abstract
This review paper investigates the extensive role of graph theory as a unifying framework for network analysis across diverse domains. The study begins by out- lining fundamental concepts, such as adjacency matrices, centrality measures, and community detection algorithms, which together enable systematic exploration of net- work topologies. Next, it examines pivotal applications, illustrating how graph-based techniques facilitate tasks like influencer detection in social media, energy-efficient routing in communication networks, and large-scale protein-interaction modeling in bioinformatics. Methodologically, the paper consolidates theoretical foundations with real-world case studies, highlighting both classical graph models (e.g., Erd˝os–R´enyi, Watts–Strogatz, Barab´asi–Albert) and advanced solutions (e.g., graph neural networks and quantum walks) that address emerging challenges of dynamic, multilayered, and high-dimensional data. The key findings demonstrate that graph theory consistently delivers actionable in- sights—enhancing traffic management in transportation, bolstering fault tolerance in critical infrastructures, and supporting cutting-edge cybersecurity anomaly detection. Moreover, the exploration of hypergraphs and quantum computing signals promising avenues for further research. In practical terms, the ability to handle massive datasets in near-real-time has positioned graph analysis as an essential tool for academia, indus- try, and public policy. Overall, this study underscores the versatility of graph theory and points to new interdisciplinary opportunities, emphasizing the need for continued innovation in handling computational complexity, data privacy, and dynamic network evolution.
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References
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