Graph mapping: Multi-scale community visualization of massive graph data. (July 2017)

• Record Type: Journal Article
• Authors: Jonker, David; Langevin, Scott; Giesbrecht, David; Crouch, Michael; Kronenfeld, Nathan
• Is Part Of: Information visualization. Volume 16:Number 3(2017)
• Page Start: 190
• Page End: 204
• Publication Date: 2017-07
• Subjects: Graph/network data -- data clustering -- distributed computing -- scalability issues -- interaction design -- zooming and navigation techniques -- multi-scale visualization -- visualization of graphs -- visual analytics -- large graph visualization; Information visualization -- Periodicals; 006.605
• DOI: 10.1177/1473871616661195
• Languages: English
• ISSNs: 1473-8716

Abstract

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.