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 Gradoop, 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. Gradoop 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.
Best Demo Award, BTW 2017