OPTIMIZATION OF GRAPH SEARCH ALGORITHMS FOR SOCIAL NETWORKS
Keywords:
social networks, graph search, algorithm optimization, genetic algorithms, tabu search, particle swarmAbstract
This article examines the potential of algorithm optimization techniques applied to graph search in social networks. The proliferation of these platforms and the large volumes of data they generate pose critical challenges in terms of computational efficiency and functionality. Through a comprehensive literature review, methods such as genetic algorithms, tabu search and particle swarm optimization are evaluated. The results reveal substantial improvements in processing speed, search accuracy and pattern identification capability. These optimizations have an impact on a better user experience, personalized recommendations and community detection. However, caution is required in the implementation to avoid unwanted effects on privacy and human autonomy. In conclusion, the techniques analyzed offer a promising path towards more efficient and people-centric social networks, but more research is needed on their long-term impact.
