Distributed Evolutionary Algorithm for Clustering Multi-Characteristic Social Networks

In this information era, data from different sources (online activities) are in abundance. Social media are increasingly providing activities and data, relations and interactions (audio, video and texting) among social actors (people), due to increasing capabilities of mobile devices and the ease access to the Internet. More than a billion people are now involved in online social media, and analyzing these interactive structures is a huge data-analytic problem. The primary focus of this work is to develop a clustering algorithm for multi-characteristic and dynamic online social networks. This work uses a combination of multi-objective evolutionary algorithms, distributed file systems and nested hybrid-indexing techniques to cluster the multi-characteristic dynamic social networks. Empirical results demonstrate that this adaptive clustering of dynamic social interactions can also provide a reliable distributed framework for BIG data analysis.