Interactive Graph Layout of a Million Nodes
Journal Title: Informatics - Year 2016, Vol 3, Issue 4
Abstract
Sensemaking of large graphs, specifically those with millions of nodes, is a crucial task in many fields. Automatic graph layout algorithms, augmented with real-time human-in-the-loop interaction, can potentially support sensemaking of large graphs. However, designing interactive algorithms to achieve this is challenging. In this paper, we tackle the scalability problem of interactive layout of large graphs, and contribute a new GPU-based force-directed layout algorithm that exploits graph topology. This algorithm can interactively layout graphs with millions of nodes, and support real-time interaction to explore alternative graph layouts. Users can directly manipulate the layout of vertices in a force-directed fashion. The complexity of traditional repulsive force computation is reduced by approximating calculations based on the hierarchical structure of multi-level clustered graphs. We evaluate the algorithm performance, and demonstrate human-in-the-loop layout in two sensemaking case studies. Moreover, we summarize lessons learned for designing interactive large graph layout algorithms on the GPU.
Authors and Affiliations
Peng Mi, Maoyuan Sun, Moeti Masiane, Yong Cao and Chris North
Multiple-Criteria Decision Support for a Sustainable Supply Chain: Applications to the Fashion Industry
With increasing globalization and international cooperation, the importance of sustainability management across supply chains has received much attention by companies across various industries. Companies therefore stri...
Human–Information Interaction with Complex Information for Decision-Making
Human–information interaction (HII) for simple information and for complex information is different because people’s goals and information needs differ between the two cases. With complex information, comprehension com...
Frame-Based Elicitation of Mid-Air Gestures for a Smart Home Device Ecosystem
If mid-air interaction is to be implemented in smart home environments, then the user would have to exercise in-air gestures to address and manipulate multiple devices. This paper investigates a user-defined gesture vo...
Using Introspection to Collect Provenance in R
Data provenance is the history of an item of data from the point of its creation to its present state. It can support science by improving understanding of and confidence in data. RDataTracker is an R package that coll...
Selective Wander Join: Fast Progressive Visualizations for Data Joins
Progressive visualization offers a great deal of promise for big data visualization; however, current progressive visualization systems do not allow for continuous interaction. What if users want to see more confident...