Parallel Graph Analytics. - LENHARTH, ANDREW; NGUYEN, DONALD; PINGALI, KESHAV (Communications of the ACM, 2016)
The article explains data-centric abstractions and execution strategies to exploit parallelism in large-scale graph analytics. It describes the categories of graphs including planar graphs, social network graphs and random graphs and illustrates the concepts of data-centric abstraction of algorithms using the Dijkstra and Bellman-Ford algorithms for the singe-source shortest path problem and collaborative filtering. It outlines vital choices in implementing parallel graph analytics programs.