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I am delighted to see Stivala’s piece on geodesic cycle length, which responds to and goes considerably beyond my 2017 JOSS. This article (1) regularizes the terminology I used; (2) replicates my analyses using exponential random graph models; and (3) applies these models to other data sets to examine the degree to which these models predict geodesic cycle length. All of these constitute a welcome (and impressively done) contribution. Yet, I also have a sense that some of the motivation of this paper is to establish the superiority of the ERGM approach, and to treat all others as, at best, fallbacks. Given that part of my reason to write the first paper was precisely to try to help us avoid the monoculture that I see developing with the use of ERGMs, Stivala’s contribution provides an excellent opportunity for social networkers to consider the implications and strengths of different models, and different ways of understanding our task as analysts.

Comment on Geodesic Cycle Length Distributions in Delusional and Other Social Networks