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
Thinking about The Information Age
“It is a pleasure to open the Information Age exhibition today at the @ScienceMuseum and I hope people will enjoy visiting. Elizabeth R.” The museum proudly claims that the exhibition had been several years in planning...
Visualizing 3D Terrain, Geo-Spatial Data, and Uncertainty
Visualizing geo-spatial data embedded into a three-dimensional terrain is challenging. The problem becomes even more complex when uncertainty information needs to be presented as well. This paper addresses the question o...
ETL Best Practices for Data Quality Checks in RIS Databases
The topic of data integration from external data sources or independent IT-systems has received increasing attention recently in IT departments as well as at management level, in particular concerning data integration...
From Offshore Operation to Onshore Simulator: Using Visualized Ethnographic Outcomes to Work with Systems Developers
This paper focuses on the process of translating insights from a Computer Supported Cooperative Work (CSCW)-based study, conducted on a vessel at sea, into a model that can assist systems developers working with simulato...
Reinforcement Learning for Predictive Analytics in Smart Cities
The digitization of our lives cause a shift in the data production as well as in the required data management. Numerous nodes are capable of producing huge volumes of data in our everyday activities. Sensors, personal...