A comparison of two approaches to fitting directed graphs to nonsymmetric proximity measures

Two algorithms for fitting directed graphs to nonsymmetric proximity data are compared. The first approach, termed MAPNET, is a direct extension of a mathematical programming procedure for fitting undirected graphs to symmetric proximity data presented by Klauer and Carroll (1989). For a user-specified number of links, the algorithm seeks to provide the connected network that gives the least-squares approximation of the proximity data with the specified number of links, allowing for linear transformations of the data. The mathematical programming approach is compared to the NETSCAL method for fitting directed graphs (Hutchinson 1989), using the Monte Carlo methods and data sets employed by Hutchinson.

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