Optimizing Graph Theory Algorithms for Social Network Analysis

Abstract

Social networks that exist in real-life and virtual spaces show complex structures from entity-to-entity connections. The paper demonstrates optimization methods which target graph theory algorithms for social network analysis (SNA) to improve their performance effectiveness. The research evaluates optimized algorithms through a comparison with standard techniques while processing large-scale data collections. The analytical methods produce substantial enhancements of computation speed along with memory management and precision handling capabilities which enables scalable real-time social network analysis.