The Big Picture: Understanding large-scale graphs using Graph Grouping with GRADOOP

Abstract

Graph grouping supports data analysts in decision making based on the characteristics of large-scale, heterogeneous networks containing millions or even billions of vertices and edges. We demonstrate graph grouping with G , a scalable system supporting declarative programs composed from multiple graph operations. Using social network data, we highlight the analytical capabilities enabled by graph grouping in combination with other graph operators. The resulting graphs are visualized and visitors are invited to either modify existing or write new analytical programs. G is implemented on top of Apache Flink, a state-of-the-art distributed dataflow framework, and thus allows us to scale graph analytical programs across multiple machines. In the demonstration, programs can either be executed locally or remotely on our research cluster.