A Review on Large Scale Graph Processing Using Big Data Based Parallel Programming Models

Processing big graphs has become an increasingly essential activity in various fields like engineering, business intelligence and computer science. Social networks and search engines usually generate large graphs which demands sophisticated techniques for social network analysis and web structure mining. Latest trends in graph processing tend towards using Big Data platforms for parallel graph analytics. MapReduce has emerged as a Big Data based programming model for the processing of massively large datasets. Apache Giraph, an open source implementation of Google Pregel which is based on Bulk Synchronous Parallel Model (BSP) is used for graph analytics in social networks like Facebook. This proposed work is to investigate the algorithmic effects of the MapReduce and BSP model on graph problems. The triangle counting problem in graphs is considered as a benchmark and evaluations are made on the basis of time of computation on the same cluster, scalability in relation to graph and cluster size, resource utilization and the structure of the graph.