I am grateful to John Levi Martin for both his fascinating original paper (Martin, 2017) in JoSS which inspired my contribution (Stivala 2020), and for his thought-provoking (and entertaining) Comment (Martin 2020). As Martin notes in his Comment, he had the sense from the original version of my manuscript which he saw as a reviewer, that part of my motivation was to establish the superiority of the exponential random graph model (ERGM) approach over others, and specifically the dk
Journal of Social Structure , ISSUE 1, 94–106
local and spatial.
In this work, I will re-examine these networks using a different random graph model, the exponential random graph model (ERGM), which allows some more flexibility in the structures it can model, and also allows use of additional data (such as nodal covariates) not available in the purely structural dk-series model. In addition, I will make precise the “hollow ring” definition and put it in the context of mathematics and computer science research in graph theory, where it is known
Journal of Social Structure , ISSUE 1, 35–76
Connections , ISSUE 1-2, 69–84
models given by the “Statnet:ergm” package in R software. The goodness of fit of the model is judged by the fit of degree distributions. I use p-values to assess the significance of the model parameters. Many argue that the p-value has a meaning only in relation to what it could mean in the data and to the external context of the analysis, thus having a strict approach and regarding a parameter as significant only up to 0.05 level can be unnecessary and limiting. In addition, even though the ERGM
Connections: The Quarterly Journal , ISSUE 1, 1–20