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2017 IEEE Pacific Visualization Symposium (PacificVis) (2017)
Seoul, South Korea
April 18, 2017 to April 21, 2017
ISSN: 2165-8773
ISBN: 978-1-5090-5739-9
pp: 1-10
Jun Tao , University of Notre Dame, United States of America
Jian Xu , University of Notre Dame, United States of America
Chaoli Wang , University of Notre Dame, United States of America
Nitesh V. Chawla , University of Notre Dame, United States of America
Unlike the conventional first-order network (FoN), the higher-order network (HoN) provides a more accurate description of transitions by creating additional nodes to encode higher-order dependencies. However, there exists no visualization and exploration tool for the HoN. For applications such as the development of strategies to control species invasion through global shipping which is known to exhibit higher-order dependencies, the existing FoN visualization tools are limited. In this paper, we present HoNVis, a novel visual analytics framework for exploring higher-order dependencies of the global ocean shipping network. Our framework leverages coordinated multiple views to reveal the network structure at three levels of detail (i.e., the global, local, and individual port levels). Users can quickly identify ports of interest at the global level and specify a port to investigate its higher-order nodes at the individual port level. Investigating a larger-scale impact is enabled through the exploration of HoN at the local level. Using the global ocean shipping network data, we demonstrate the effectiveness of our approach with a real-world use case conducted by domain experts specializing in species invasion. Finally, we discuss the generalizability of this framework to other real-world applications such as information diffusion in social networks and epidemic spreading through air transportation.
Ports (Computers), Marine vehicles, Data visualization, Trajectory, Tools, Complex systems, Electronic mail
Jun Tao, Jian Xu, Chaoli Wang, Nitesh V. Chawla, "HoNVis: Visualizing and exploring higher-order networks", 2017 IEEE Pacific Visualization Symposium (PacificVis), vol. 00, no. , pp. 1-10, 2017, doi:10.1109/PACIFICVIS.2017.8031572
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