Filip Agneessens is a Senior Lecturer at the Surrey Business School (University of Surrey) and an associate member of the Department of Sociology (University of Oxford). He has published widely on social network analysis in leading social science journals, including the journal Social Networks. He was a guest-editor for a special issue on two-mode social network analysis in the journal Social Networks and is currently guest-editing a second special issue on negative and signed networks. He is also currently working on a chapter for the Oxford Handbook of Social Networks.

He has previously taught a number of courses on network theory, social network methods and social network analysis applied to organisations, sociology and political sciences. His research centres on social network analysis with a specific focus on methodology/statistics and applications to organisational settings. He is especially interested in how network relations come about, and how they subsequently impacts outcomes (such as performance and well-being). He has also been working on mathematical models for analysing complete networks and on typologies for social support (ego-networks).

**Course Content **

This module covers advanced statistical methods for analyzing social network data, focusing on testing hypotheses about network structure (e.g. reciprocity, transitivity, and closure), and the formation of ties based on attributes (e.g. homophily). The first three days provide an in depth discussion of exponential random graph models (also known as ERGM or p* models). We then introduce longitudinal models such as RSiena models (SAOMs) and relational event models.

Software used includes: MPNet and R packages RSiena and relevant.

**Course Objectives**

The module aims to familiarize participants with the formal statistical analysis of network data for selection mechanisms, as well as selection and influence in longitudinal models. Participants will become familiar with specific programmes designed for these analyses and with the mathematical basis for the modelling approaches, and they will learn how to conduct statistical analyses of their own network data. Participants are encouraged to bring with them their own network data to be analyzed using the techniques covered. The course provides a thorough, yet intuitive introduction to these models and does not require advanced statistical knowledge. It does require basic knowledge of logistic regression and the principle of significance tests in classic (survey) research.

In general the module focuses on how do the characteristics of a network of interest differ from chance? At the end of the module, participants should be able to answer questions, such as:

–Is there more reciprocity in an advice network than could be expected by chance?

–Is there a tendency towards homophily? (do smokers tend to be friends with other smokers? and do non- smokers tend to be friends with other non-smokers)?

–Is there more transitivity (are friends of friends also friends) or closure or cyclicality in a network than expected by chance, controlling for the degree distribution?

–Do advice and friendship ties tend to overlap (multiplexity)?

–Considering the friendship network for different classes at the same time, is there an overall tendency towards clustering? Are there differences in tendency between classes?

**Course Prerequisites**

Participants should have taken an introductory course in social network analysis, so be familiar with such terms as reciprocity, density, indegree and outdegree. Participants should also have taken a basic module in (logistic) regression analysis

**Remedial Reading **

Scott, J. 1992. Social Network Analysis. Sage.

**Representative Background Reading**

Wasserman, S., and Faust, K. 1996. Social Network Analysis. Cambridge University Press.

**Required Reading**

Lusher, D., Koskinen, J., and G. Robins. Editors. 2013. Exponential Random Graph Models for Social Networks. Cambridge University Press.

**Background knowledge required***Statistics*

OLS = e

Maximum Likelihood = e

*Computer Background*

R = e

e = elementary, m = moderate, s = strong