An Effective Anytime Anywhere Parallel Approach for Centrality Measurements in Social Network Analysis

With the broad application of electronic communication monitoring tools and data-sharing techniques, the size of networks to be studied by social network analysis (SNA) has grown rapidly. However, current SNA techniques are not particularly scalable. For example, even centrality, which is one of the most frequently used SNA parameters, cannot be measured by most current SNA software when the network is large. This paper presents the design of an effective and scalable anytime anywhere parallel methodology for SNA with large-scale networks emphasizing centrality measurement algorithms. The efficiency and effectiveness of the methodology is validated by experiments of centrality analysis for large networks.