Graph Mapping: Multi-Scale Community Visualization Of Massive Graph Data

Graph visualizations increase the perception of entity relationships in a network. However, as graph size and density increases, readability rapidly diminishes. In this article, we present an end-to-end, tile-based visual analytic approach called graph mapping that utilizes cluster computing to turn large-scale graph (node-link) data into interactive visualizations in modern web browsers. Our approach is designed for end-user analysis of community structure and relationships at macro-and micro scales. We also present the results of several experiments using alternate methods for qualitatively improving comprehensibility of hierarchical community detection visualizations by proposing constraints to state-of-the-art modularity maximization algorithms.