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Measuring the Goodness of Fit of Link Reduction Algorithms for Mapping Intellectual Structures in Bibliometric Analysis

Journal of the Korean Society for Information Management / Journal of the Korean Society for Information Management, (P)1013-0799; (E)2586-2073
2022, v.39 no.2, pp.233-254
https://doi.org/10.3743/KOSIM.2022.39.2.233
Jae Yun Lee (Myongji University)
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Abstract

Link reduction algorithms such as pathfinder network are the widely used methods to overcome problems with the visualization of weighted networks for knowledge domain analysis. This study proposed NetRSQ, an indicator to measure the goodness of fit of a link reduction algorithm for the network visualization. NetRSQ is developed to calculate the fitness of a network based on the rank correlation between the path length and the degree of association between entities. The validity of NetRSQ was investigated with data from previous research which qualitatively evaluated several network generation algorithms. As the primary test result, the higher degree of NetRSQ appeared in the network with better intellectual structures in the quality evaluation of networks built by various methods. The performance of 4 link reduction algorithms was tested in 40 datasets from various domains and compared with NetRSQ. The test shows that there is no specific link reduction algorithm that performs better over others in all cases. Therefore, the NetRSQ can be a useful tool as a basis of reliability to select the most fitting algorithm for the network visualization of intellectual structures.

keywords
link reduction algorithm, network visualization, mapping intellectual structure, pathfinder network, clustering-based network, goodness of fit
Submission Date
2022-05-15
Revised Date
2022-06-07
Accepted Date
2022-06-07

Journal of the Korean Society for Information Management