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Symmetry 2019, 11(1), 30; https://doi.org/10.3390/sym11010030

Applying Graph Centrality Metrics in Visual Analytics of Scientific Standard Datasets

1
Faculty of Engineering and Information Technology, Shaoyang University, Shaoyang 422000, China
2
School of Software, University of Technology Sydney, Sydney 2000, Australia
3
School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne 3122, Australia
*
Author to whom correspondence should be addressed.
Received: 6 October 2018 / Revised: 13 December 2018 / Accepted: 25 December 2018 / Published: 1 January 2019
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Abstract

Graphs are often used to model data with a relational structure and graphs are usually visualised into node-link diagrams for a better understanding of the underlying data. Node-link diagrams represent not only data entries in a graph, but also the relations among the data entries. Further, many graph drawing algorithms and graph centrality metrics have been successfully applied in visual analytics of various graph datasets, yet little attention has been paid to analytics of scientific standard data. This study attempts to adopt graph drawing methods (force-directed algorithms) to visualise scientific standard data and provide information with importance ‘ranking’ based on graph centrality metrics such as Weighted Degree, PageRank, Eigenvector, Betweenness and Closeness factors. The outcomes show that our method can produce clear graph layouts of scientific standard for visual analytics, along with the importance ‘ranking’ factors (represent via node colour, size etc.). Our method may assist users with tracking various relationships while understanding scientific standards with fewer relation issues (missing/wrong connection etc.) through focusing on higher priority standards. View Full-Text
Keywords: visual analytics; graph drawing; graph centrality metrics; scientific standard datasets; force-directed algorithms visual analytics; graph drawing; graph centrality metrics; scientific standard datasets; force-directed algorithms
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Hua, J.; Huang, M.L.; Huang, W.; Zhao, C. Applying Graph Centrality Metrics in Visual Analytics of Scientific Standard Datasets. Symmetry 2019, 11, 30.

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