Inferential Network Analysis

This course revolves around the idea of creating probabilistic statistical models of networks. This is a big departure from the descriptive analysis of networks (e.g., measuring the centrality of a node) and also a fairly big departure from the statistical modelling of non-network data with the regression framework.

Our goal is to develop statistical models that can accomplish the same general objectives as regression models (fitting parameters to data with probabilistic models), while accounting for the substantial endogenous complexity inherent to network data.

To accomplish this, we will consider two basic approaches to modelling networks.

The approach on which we will spend the most time involves explicitly modelling the network dependencies present in the data. Starting cross-sectionally, we will introduce the exponential random graph model (ERGM) and consider it in some detail – including specification, estimation, fit checking, diagnosing problems, limitations, and post-estimation analysis and interpretation. We will then extend our knowledge of this approach to longitudinal, repeatedly observed networks by considering the Temporal ERGM and the stochastic actor-oriented model (SAOM, more commonly known as SIENA), which are closely related.

We close by considering alternative approaches to modelling networks, including the latent space network model and the quadratic assignment procedure, in which network dependencies are projected into the error term rather than explicitly modelled.

We will also discuss two additional models for temporal data: the temporal network autocorrelation model (TNAM), which is a model for the behaviour of nodes in a network, and the relational event model (REM), which can model a temporally more fine-grained series of network ties.

For each topic we cover, we will also consider how to perform such analyses in R using several example datasets.

If you want, you can present your own problems in class, and benefit from constructive feedback from the group.

What you will learn

This course will enable you to think about your own network data from a statistical and theoretical point of view. You will learn how to translate their theoretical questions into statistical models and how to answer these questions using empirical data and estimation.

This is an advanced course, which assumes existing knowledge of basic social science research methods, at least through generalised linear models (logit, the linear model, etc...) as well as basic knowledge of network analysis.

You should know the basic anatomy of networks as well as the descriptive tools of network analysis (e.g., measures of centrality, plotting and visualisation, etc...).

All techniques will be demonstrated using the R statistical language.

While this is not a course *about* software, basic familiarity with R is helpful, because I will not go into detail about how to load/manage data or use R's more basic functions. That said, a high level of R (e.g., programming competency) is not necessary.

Please install R and the packages statnet and xergm before the course.

Each course includes pre-course assignments, including readings and pre-recorded videos, as well as daily live lectures totalling at least three hours. The instructor will conduct live Q&A sessions and offer designated office hours for one-to-one consultations.

Please check your course format before registering.

Online courses

Live classes will be held daily for three hours on a video meeting platform, allowing you to interact with both the instructor and other participants in real-time. To avoid online fatigue, the course employs a pedagogy that includes small-group work, short and focused tasks, as well as troubleshooting exercises that utilise a variety of online applications to facilitate collaboration and engagement with the course content.

In-person courses

In-person courses will consist of daily three-hour classroom sessions, featuring a range of interactive in-class activities including short lectures, peer feedback, group exercises, and presentations.

Disclaimer

This course description may be subject to subsequent adaptations (e.g. taking into account new developments in the field, participant demands, group size, etc.). Registered participants will be informed at the time of change.

By registering for this course, you confirm that you possess the knowledge required to follow it. The instructor will not teach these prerequisite items. If in doubt, please contact us before registering.