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Netscal: A Network Scaling Algorithm for Nonsymmetric Proximity Data

Published online by Cambridge University Press:  01 January 2025

J. Wesley Hutchinson
J. Wesley Hutchinson*
Affiliation:
University of Florida
*
Requests for reprints should be sent to Wes Hutchinson, Department of Marketing, 205 Matherly Hall, University of Florida, Gainesville, FL 32611.
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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.

Keywords

networksproximity scalingsimilaritygraph theory
Type
Original Paper
Information
Psychometrika , Volume 54 , Issue 1 , March 1989 , pp. 25 - 51
Copyright
Copyright © 1989 The Psychometric Society

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Footnotes

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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