Abstract
Social network analysis (SNA) leverages graph theory to understand and visualize the complex relationships and structures within social networks. This research paper explores the optimization of graph theory algorithms tailored for SNA, focusing on efficiency improvements in handling large-scale networks. The study reviews key graph theory concepts, identifies common challenges in SNA, and evaluates various optimization techniques. Practical applications and case studies are presented to demonstrate the impact of these optimizations in real-world scenarios.
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