Graph mapping: Multi-scale community visualization of massive graph data
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Aug 11, 2016
- Source ID
- 10.1177/1473871616661195
Entities
People
- David Giesbrecht
- David Jonker
- Michael Crouch
- Nathan Kronenfeld
- Scott Langevin
Organizations
- Defense Advanced Research Projects Agency