Duality of departmental specializations and PhD exchange: A Weberian analysis of status in interaction using multilevel exponential random graph models (mERGM)

A statistical model for the analysis of mobility tables as weighted networks with an application to faculty hiring networks

“…Beyond these examples, the model could be applied to international migration (as in the example by Windzio, 2018), or inter-or intra-organisational mobility and career paths (extending analyses such as Woldense, 2018;Gondal, 2018). In each case the contributions of a tailor-made statistical model might uncover more detailed mechanisms that guide the respective mobility decisions.…”

“…The number of mobile individuals between two locations determines the edge weight. Examples of empirical analyses of such mobility networks include individuals' change in occupation over the life-course (Toubøl and Larsen, 2017;Cheng and Park, 2020), inter-generational mobility between social classes (Melamed, 2015), migration patterns between countries (Windzio, 2018), residential moves between neighbourhoods (Müller et al, 2018), patients transfers between hospitals (Stadtfeld et al, 2016), hiring of graduates between universities (Clauset et al, 2015;Gondal, 2018), and mobility of government officials between branches (Woldense, 2018). 1 The varying mobility issues outlined above are treated as network questions for one of two broad reasons-first, to explore the data with descriptive networks analyses, or, second, to uncover dependence structures in these mobility networks with statistical network models.…”

“…Similarly, Gondal (2018) shows a strong indegree centralisation among PhD hiring networks of US sociology departments, representing few popular and many unpopular departments. Finally, Müller et al (2018) model mobility between Stockholm neighbourhoods and find transitive clustering in mobility but an tendency against forming cycles, indicating a prestige hierarchy in the present case.…”

Exponential-Family Models of Random Graphs: Inference in Finite-, Super-, and Infinite Population Scenarios

“…Curved ERGMs with geometrically weighted model terms are well-posed as long as θ 3 ≥ 0; note that θ 3 ∈ [− log 2, 0) implies that the added value of the m-th triangle either decreases or increases, depending on the sign of θ 2 and whether m is even or odd, and that θ 3 ∈ (−∞, − log 2) implies a form of model near-degeneracy when |N| is large (Schweinberger, 2011). In practice, curved ERGMs with GWESP terms and other geometrically weighted model terms have turned out to be considerably better-behaved than the triangle model: selected applications can be found in Snijders et al (2006), Hunter and Handcock (2006), Hunter (2007), Hunter, Goodreau and, Goodreau, Kitts and Morris (2009), Gile and Handcock (2006), Handcock and Gile (2010), Koskinen, Robins and Pattison (2010), Simpson, Hayasaka and Laurienti (2011), Suesse (2012), Rolls et al (2013), Wang et al (2013), Almquist and Bagozzi (2015), Obando andDe Vico Fallani (2017), andGondal (2018). We apply curved ERGMs to human brain network data in Section 8, demonstrating that curved ERGMs outperform both Bernoulli random graphs and latent space models.…”

“…There is a large and growing body of work on multilevel network data and models (e.g., Lubbers, 2003;Wang et al, 2013;Zappa and Lomi, 2015;Lomi, Robins and Tranmer, 2016;Slaughter and Koehly, 2016;Hollway and Koskinen, 2016;Lazega and Snijders, 2016;Wang et al, 2016a;Brailly et al, 2016 Hollway et al, 2017;Gondal, 2018).…”