2017 IEEE Pacific Visualization Symposium (PacificVis) (2017)
Seoul, South Korea
April 18, 2017 to April 21, 2017
Quan Hoang Nguyen , The School of Information Technologies, University of Sydney, Australia
Seok-Hee Hong , The School of Information Technologies, University of Sydney, Australia
Peter Eades , The School of Information Technologies, University of Sydney, Australia
This paper introduces a new kind of geometric graph, called the degree-sensitive neighbourhood graph (dNNG), for a more precise modelling of neighbourhoods. Based on dNNG, we define better shape-based metrics and then propose a neighbourhood-driven force-directed algorithm, called NEFO, for neighbourhood faithfulness. Our evaluation on both real-world and randomly generated graphs shows that the dNNG gives more effective shape-based measures when compared to existing geometric graphs. The NEFO algorithm is shown to be effective for improving neighbourhood faithfulness of graph drawings.
Layout, Shape measurement, Force, Optimization, Shape, Visualization
Q. H. Nguyen, S. Hong and P. Eades, "dNNG: Quality metrics and layout for neighbourhood faithfulness," 2017 IEEE Pacific Visualization Symposium (PacificVis)(PACIFICVIS), Seoul, South Korea, 2017, pp. 290-294.