Efficiency Improvements in Social Network Communication via MapReduce

As we are living in a "smart world" (which comprises cyber, physical and social worlds), big data are everywhere. High volumes of high-veracious, high-valuable data can be easily generated and collected at a high velocity from a high variety of data sources in various real-life applications in the fields of sciences and engineering, finance, social media, as well as online information resources. These big data have become an increasingly decisive resource in the modern society. Embedded in these big data are rich sets of useful information and knowledge. Hence, data intensive systems that provide data science solutions are in demand. In this paper, we propose a system that applies the MapReduce programming model to improve communication in social networks. Experimental results show the efficiency and effectiveness of the two improvement methods used in our proposed social system in reducing the number of communication hubs. These efficiency improvements not only lead to practical social network communications but also lead to the emergence of the cyber-physical-social interaction and computing.