Discovering Dense Subgraphs in Parallel for Compressing Web and Social Networks

Mining and analyzing graphs are challenging tasks, especially with today's fast-growing graphs such as Web and social networks. In the case of Web and social networks an effective approach have been using compressed representations that enable basic navigation over the compressed structure. In this paper, we first present a parallel algorithm for reducing the number of edges of Web graphs adding virtual nodes over a cluster using BSP (Bulk Synchronous Processing) model. Applying another compression technique on edge-reduced Web graphs we achieve the best state-of-the-art space/time tradeoff for accessing out/in-neighbors. Second, we present a scalable parallel algorithm over BSP for extracting dense subgraphs and represent them with compact data structures. Our algorithm uses summarized information for implementing dynamic load balance avoiding idle time on processors. We show that our algorithms are scalable and keep compression efficiency.