Analyzing Dynamic Hypergraphs with Parallel Aggregated Ordered Hypergraph Visualization

• Published in: IEEE transactions on visualization and computer graphics Vol. 27; no. 1; pp. 1 - 13
• Main Authors: Valdivia, Paola, Buono, Paolo, Plaisant, Catherine, Dufournaud, Nicole, Fekete, Jean-Daniel
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Apexes; Authoring; case study; Computational modeling; Computer Science; Contracts; digital humanities; dynamic graph; dynamic hypergraph; Graph theory; History; Human-Computer Interaction; Humanities and Social Sciences; interaction; Methods and statistics; Motion pictures; Topology; Usability; usability <bold xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"/>; Visualization; interaction; usability; Index Terms-dynamic graph; dynamic hypergraph; case study; digital humanities
• ISSN: 1077-2626; 1941-0506
• DOI: 10.1109/TVCG.2019.2933196

Parallel Aggregated Ordered Hypergraph(PAOH) is a novel technique to visualize dynamic hypergraphs. Hypergraphs are a generalization of graphs where edges can connect several vertices. Hypergraphs can be used to model networks of business partners or co-authorship networks with multiple authors per article. A dynamic hypergraph evolves over discrete time slots. PAOH represents vertices as parallel horizontal bars and hyperedges as vertical lines, using dots to depict the connections to one or more vertices. We describe a prototype implementation of Parallel Aggregated Ordered Hypergraph, report on a usability study with 9 participants analyzing publication data, and summarize the improvements made. Two case studies and several examples are provided. We believe that PAOH is the first technique to provide a highly readable representation of dynamic hypergraphs. It is easy to learn and well suited for medium size dynamic hypergraphs (50-500 vertices) such as those commonly generated by digital humanities projects-our driving application domain.