Social Network Analysis of Cricket Community Using a Composite Distributed Framework: From Implementation Viewpoint

This paper proposes an alternate ranking system based on social network metrics and their evaluation in a composite distributed framework. The most important aspect of social network domain is voluminous data. In order to know key trends, predictive analysis of huge sets of raw data can be effective. Again these analytics are generally very compute intensive. The most efficient solution of such compute-intensive analysis is to map such problems in a distributed domain. The main contributions of this paper are twofold. First, the analysis of cricket community from the viewpoint of social network and finding ranking of players and countries based on the properties of graph centrality measures. Second, the paper proposes a comprehensive distributed framework to offer infrastructural support for the large data analysis as well as graph processing. Hadoop is an open-source framework to store and process large data sets in a cloud environment. MapReduce is a popular programming model for processing that data in a distributed manner. However, MapReduce alone is not efficient for graph processing. Giraph is an alternative programming paradigm for graph processing in Hadoop. Using a practical case study of social network analysis of cricket community, this paper captures the significance of the alternate ranking in this sport, as well as shows effectiveness of the proposed framework in the process of analyzing voluminous data.