A survey on social networking using concept of evolutionary algorithms and big data analysis

Abstract

The primary motive of this survey is to impart complete analysis of social connectivity issues using concepts of big data analysis. Issues related to social networking include community detection, cluster analysis, personalized search, anomaly detection, searching cut space, friendship selection, load balancing, structural balancing, etc. These issues can be improvised using machine learning techniques including particle swarm optimization, evolutionary algorithms, genetic algorithm, properties of graphs such as embeddedness and triadic closure. These properties and algorithms enhance the behavior of social networking issues when applied to datasets of various social networking platforms.