Efficient Social Network Analysis In Big Data Architectures

Social network analysis (SNA) is the application of graph theory to understand, categorize and quantify relationships in a social network. It can be a great tool to improve analytic capabilities in any field, for example marketing analytics, churn prediction, health care, etc. In terms of SNA, network structure is defined by nodes, edges and metrics which quantify the importance or influence of certain nodes in the network or relationship strength between nodes. Algorithms for network metrics calculation are complex and that makes SNA difficult to implement in big data environments on large datasets with many nodes and edges. In this paper we will elaborate how to efficiently and performance wise perform SNA and visualize results of the analysis on large datasets using increasingly popular GraphX and JavaScript libraries.