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In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

A 3-Day Livestream Seminar Taught by Filip Agneessens, Ph.D.

Interest in social networks has been climbing exponentially since the 1970s. For social scientists, networks can be seen as a fundamental adaptation that facilitates the coordination and distribution of resources, while simultaneously maintaining the flexibility of independent agents. For physical scientists, networks can be seen as universal structures underlying everything from molecules to galaxies. And for mathematicians and computer scientists, networks provide an abstract and propitious way of representing problems.

The field of social network analysis consists of a vocabulary of theoretical and mathematical concepts for investigating network phenomena, along with a distinctive data model and set of methodologies for collecting and analyzing network data. This course provides an overview of the core concepts and methods in social network analysis.

At a theoretical level, we discuss such ideas as Granovetter’s “strength of weak ties” and “small worlds”. We also discuss core concepts, such as structural holes, homophily and transitivity, as well as popular measures like closeness centrality, betweenness centrality, centralization, fragmentation, compactness, and structural equivalence. We will evaluate when specific measures might or might not be useful to answer a particular research question.

The course is very hands-on, emphasizing mastering the software and using the concepts and methods to answer research questions. To conduct social network analysis, we will make use of functions available in R. The core reading will be Analyzing Social Networks with R by Borgatti, Everett, Johnson and Agneessens (2022).

We start this course by providing an overview of basic concepts, distinguishing between different types of networks, and then showing ways to visualize social networks with R. We also focus on measures of centrality, discuss the different types of research questions they can help answer, and how they can be calculated with R.

On the second day, we focus on some major theories, including Heider’s balance theory, Granovetter’s strength of weak ties, small worlds, and structural holes. We then go on to explore ways to measure structural holes and provide an overview of ego-network measures focusing on nodal attributes. Next, we turn to network-level measures, including measures of cohesion, centralization, reciprocity and transitivity.

On the third day, we explore different ways to identify subgroups and communities. We also touch on ways to deal with two-mode networks. Finally, we introduce the concept of equivalence and consider specific situations where different versions of equivalence might be relevant/useful. We also touch on core-periphery and blockmodeling more generally.