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Netscal: A network scaling algorithm for nonsymmetric proximity data

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Abstract

A simple property of networks is used as the basis for a scaling algorithm that represents nonsymmetric proximities as network distances. The algorithm determines which vertices are directly connected by an arc and estimates the length of each arc. Network distance, defined as the minimum pathlength between vertices, is assumed to be a generalized power function of the data. The derived network structure, however, is invariant across monotonic transformations of the data. A Monte Carlo simulation and applications to eight sets of proximity data support the practical utility of the algorithm.

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Authors and Affiliations

  1. Department of Marketing, University of Florida, 205 Matherly Hall, 32611, Gainesville, FL

    J. Wesley Hutchinson

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  1. J. Wesley Hutchinson
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Additional information

I am grateful to Roger Shepard and Amos Tversky for their helpful comments and guidance throughout this project. The work was supported by National Science Foundation Grant BNS-75-02806 to Roger Shepard and a National Science Foundation Graduate Fellowship to the author. Parts of this paper were drawn from a doctoral dissertation submitted to Stanford University (Hutchinson, 1981).

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Hutchinson, J.W. Netscal: A network scaling algorithm for nonsymmetric proximity data. Psychometrika 54, 25–51 (1989). https://doi.org/10.1007/BF02294447

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  • DOI: https://doi.org/10.1007/BF02294447

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