Rapid, Progressive Sub-Graph Explorations for Interactive Visual Analytics over Large-Scale Graph Datasets

Samuel Armstrong, Kevin Bruhwiler, Sangmi Lee Pallickara

Abstract: Exploring a voluminous graph dataset visually is a challenging task due to the sheer amount of data and the lack of structure to rely on during the navigation. Indra, our framework for large-scale graph data, provides responsive visual analytics over large-scale graph datasets. In this study, we propose a novel graph indexing scheme that pivots the view of the graph to a hierarchical structure while preserving the semantic importance of vertices within the user's analysis scenario. Indra allows users to compare and track multiple aspects of sub-graphs by supporting linked multi-views and multi-resolution operations such as drill-in and roll-ups. We have performed a set of empirical benchmarks profiling Indra and these demonstrate that several operations are executed with sub-second latency to effectively support interactive visual analytics.