Conference Agenda

Devising the underlying generating mechanism of a real-life network is difficult as, more often than not, only its snapshots are available, but not its full evolution. One candidate for the generating mechanism is preferential attachment which, in its simplest form, results in a degree distribution that follows the power law. Consequently, the growth of real-life networks that roughly display such power-law behaviour is commonly modelled by preferential attachment. However, the validity of the power law has been challenged by the presence of alternatives with comparable performance, as well as the recent findings that the right tail of the degree distribution is often lighter than implied by the body, whilst still being heavy. In this paper, we study a modified version of the model with a flexible preference function that allows super/sub-linear behaviour whilst also guaranteeing that the limiting degree distribution has a heavy tail. We relate the distributions tail heaviness directly to the model parameters, allowing direct inference of the parameters from the degree distribution alone.

Co-voting networks provide important insights into political polarization, collaboration, and alliance formation in democratic systems. Here, we examine co-voting data from the United States Senate obtained from voteview.com, covering records from the 1st to the 119th Congress. Co-voting is represented in each Congress by a network in which senators are nodes, and two senators are adjacent if they voted together above a threshold rate. Such networks are structured both by shifts in the composition of the legislature, and by changing political forces that favor different types of alliance formation; these forces may vary over time in idiosyncratic ways, while also being consistent within particular periods (or even recurring to older patterns over time). This raises the challenge of modeling network behavior in a manner that is both flexible and well-regularized. Here, we employ a hierarchical Dirichlet process exponential family random graph mixture model (DP-ERGM) to infer the drivers of co-voting patterns across sessions. Our approach allows us to model hidden sub-populations of co-voting patterns over time, while using slab-and-spike priors to induce sparsity in the set of selected effects. We examine the incidence of drivers of voting patterns over time, apparent clustering in political forces generating voting behavior in different years, and the resulting graph distributions when marginalizing across latent subgroups. Implications for both voting patterns and the exploratory use of DP-ERGMs are discussed.

Citation networks are often used to quantify science, ranging from the impact of researchers, journals, or universities over to the interdisciplinarity or disruptiveness of papers. In this talk we present relational hyperevent models (RHEM) as a general modeling framework to assess patterns in the dynamics of citation networks. RHEM can be specified, among others, with endogenous effects (e.g., citing what many others cite or citing the work of those who previously cited the own work) and with random node-level effects representing the latent popularity of papers, or researchers. In an empirical analysis of more than 500,000 published papers we assess changes in one type of effects when controlling for others and estimate their relative explanatory power.

Stochastic actor-oriented models have been developed to analyze network dynamics when data are collected in a panel design. Several estimation methods are available, with the method of moments (MoM) being the default approach. This method is computed using a stochastic approximation algorithm, where the statistics that define the moment conditions naturally correspond to the parameters. Another approach is the generalized method of moments (GMoM), which extends MoM by incorporating more statistics than parameters. Although this method has been implemented and documented in Rsiena, guidelines on when to use the GMoM and which additional statistics to include are still lacking. In this paper, we present statistical approaches to determine when the additional statistics contribute useful information beyond what is provided by the moment conditions of the regular MoM. We also discuss the conditions under which the GMoM should be preferred over MoM.

The stochastic actor-oriented model (SAOM) represents change in network panel data as the outcome of actors' decision-making. The number of such decisions, therefore, serves as a natural operationalization of sample size. When there is a single dependent variable - whether a network or an actor attribute - this sample size is the primary determinant of the precision of estimates in SAOM-based data analyses and, consequently, of statistical power.

In co-evolution models, however, the situation becomes more complex. With multiple dependent variables, also sample size becomes multidimensional. Depending on the model specification, spillover effects between dependent variables may affect the precision of estimates. In this conference presentation, I propose a conceptual clarification and present empirically informed simulation studies that illustrate the main findings.