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A novel centrality-based method for visual analytics of small-world networks

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Abstract

Nowadays, the network data that we need to deal with and make sense of are becoming increasingly large and complex. Small-world networks are a type of complex networks whose underling graphs have small diameter, shorter average path length between nodes, and a high degree of clustering structures and can be found in a wide range of scientific fields, including social networks, sociology, computer science, business intelligence, and biology. However, conventional visualization algorithms for small-work networks lead to a uniform clump of nodes or are restricted to a tree structure, making the network structure difficult to identify and analyze. This work provides a new visual analytical method to improve the situation. Different from previous methods based on spanning trees, this method first generates a weighted planar sub-network based on the measurement of network centrality metrics. A force-directed algorithm based on node-edge repulsion is then applied to visualize this sub-network into a proper layout for better understanding of the data. Finally, the remaining links are placed back to maintain the original network’s integrity. The experimental results show that compared to previous methods, the proposed method can be more effective in differentiating clusters and revealing relationship patterns among individual nodes and clusters in the network. Furthermore, the proposed method is applied to a data of the semiconductor wafer manufacturing industry as a case study. The work shows that this new approach allows users to gain useful insights into the data.

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Acknowledgements

The authors thank the anonymous referees for comments that improved the content as well as the presentation of this paper. This work has been supported in part by Ministry of Science and Technology, Taiwan, under Grants MOST 105-2628-E-009-002-MY3 and MOST 106-2221-E-009-101-MY3, and the Ministry of Education, Taiwan, under the Higher Education Sprout Project.

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Authors and Affiliations

  1. Department of Industrial Engineering and Management, National Chiao Tung University, Hsinchu, 300, Taiwan

    Chun-Cheng Lin & Sheng-Feng Wu

  2. Faculty of Transdisciplinary Innovation, University of Technology Sydney, Ultimo, NSW, 2007, Australia

    Weidong Huang

  3. Department of Forestry, National Chung Hsing University, Taichung, 402, Taiwan

    Wan-Yu Liu

  4. Innovation and Development Center of Sustainable Agriculture, National Chung Hsing University, Taichung, 402, Taiwan

    Wan-Yu Liu

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  1. Chun-Cheng Lin
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  2. Weidong Huang
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  3. Wan-Yu Liu
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  4. Sheng-Feng Wu
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Correspondence to Wan-Yu Liu.

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Lin, CC., Huang, W., Liu, WY. et al. A novel centrality-based method for visual analytics of small-world networks. J Vis 22, 973–990 (2019). https://doi.org/10.1007/s12650-019-00582-5

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