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WA109 - Relational Event Models (REM)

Instructor Details

Instructor Photo

Laurence Brandenberger

Universität Bern

Instructor Bio

Laurence is a a postdoctoral scholar at the chair of systems design at ETH Zurich, studying legislative behaviour and parliamentary networks. She gained her PhD in political science and a master's degree in sociology from the University of Bern. 

She is particularly interested in the temporal dynamics of political actors working together, examining how political actors change their political positions over time, and influence each other.

Course Dates and Times

Friday 14 February 13:00–15:00 and 15:30–18:00

Saturday 15 February 09:00–12:30 and 14:00–17:30

Prerequisite Knowledge

This course tackles advanced inferential methods in network analysis. To be able to follow it, you need basic knowledge of

  • social network analysis
  • quantitative methods (e.g., regression analysis, generalised linear models, survival analysis)
  • data management

Knowledge of inferential network models (ERGMs, SAOMs, spatial lag models or others) would also be very useful. 

All lab work is done in the statistical environment R. You should be familiar with basic R-commands (loading data, handling of data frames, etc.).

Please install R and the REM package before the course starts. 

Short Outline

This course is an introduction to Relational Event Models (REMs), inferential models that use temporally fine-grained records of social interactions to model complex interaction patterns and endogenous processes.

Examples of social interaction data include political actions (such as legislative co-sponsoring, vetoing or engaging in military actions), and email communications or online interactions (eg commenting on blogs, tweeting).

You will learn how to prepare different data types, to estimate REMs and interpret their results.

You will gain hands-on experience in modelling relational events and learn how to model theory-driven social mechanisms. 

Tasks for ECTS Credits

1 credit (pass/fail grade). Attend at least 90% of course hours, participate fully in in-class activities, and carry out the necessary reading and/or other work prior to, and after, class.

Long Course Outline

Relational event models are powerful tools for examining how social networks evolve over time. Building on event history analysis, REMs combine network dependencies with temporal dynamics and allow for the analysis of group formation patterns – such as alliance or coalition formation processes – influencing dynamics, social learning and other social mechanisms. 

REMs aim to explain the temporal order of social interactions.

Why do two people suddenly start exchanging emails?
Why do two governments start engaging in military conflicts?
Why do two members of parliament start collaborating with each other on a new legislative proposal?

The answers to these questions are sometimes found in the broader context of these events:

If two countries take up arms against each other, the alliances that formed beforehand play a key role.
If two people start exchanging emails it is possible that a mutual friend introduced them to each other.
And if two members of parliament start working on a mutual proposal it is possible that they both learned about their mutual interest by both opposing a proposal by another member.

The events that occurred in the past often guide subsequent events. The relational event point of view can help uncover not simply how previous interactions between actors a and b shape their future interactions, but also how changes in their surrounding network (i.e., with events that do not even involve a or b) affect how a and b interact in the future. It is a powerful framework for time-stamped or time-ordered analysis of social interactions that takes the surrounding context and an actor’s embeddedness into account. And so REMs build on event history analysis to ask the question: why does one event occur at time t and why has it not occurred before? And, in a broader sense, which patterns of past interactions can help explain a specific sequence of events? 

This course provides a hands-on introduction to REMs. You will learn the statistical backbone of REMs and apply them in different lab sessions. Since REMs are longitudinal models, we will pay special attention to the temporal aspects of the model, including data preparation and operationalisation of dynamic social mechanisms such as reciprocity, closure or homophily. The course also focuses on the interpretation of REMs and assessing their goodness-of-fit. All lab sessions are held in the statistical computing environment R. 

Apart from the practical aspects, the course also focuses on theory-driven research. REMs can be used to detect social influencing, understand social exchanges and communication patterns or determine causes for group formation processes. Different network concepts are presented throughout the course and we will discus their operationalisation. You are encouraged to bring your own research interests to the course and think about ways to operationalise and test your hypotheses. 

You are also welcome to bring your own network data or research ideas to discuss their potential and feasibility.

Day-to-Day Schedule

Day-to-Day Reading List

Software Requirements

We will use R and several of its packages (rem, ggplot2, xergm, survival) during the course.

Please install R (and R-Studio if desired) before the course starts. 

Hardware Requirements

Please bring your own laptop. 

Windows is ok but Mac / Linux are preferable thanks to their easy handling of parallel computing.


Additional literature on relational event models

Quintane and Carnabuci (2016)
Zenk and Stadtfeld (2010)
De Nooy and Kleinnijenhuis (2013)
DuBois, Butts and Smyth (2013)
Leifeld and Brandenberger (2019)
Quintane et al. (2013)
Pilny et al. (2016)
Liang (2014)
Welbers and de Nooy (2014)
Stadtfeld and Geyer-Schulz (2011)
Patison et al. (2015)
Tranmer et al. (2015)
Leenders, Contractor and DeChurch (2016)
Xia, Mankad and Michailidis (2016)
Pilny et al. (2017)

Literature on survival analysis

Box-Steffensmeier and Jones (2004)
Allison (2014)
Andersen and Gill (1982)
Cox and Oakes (1984)
Allison (1982)
Gail, Lubin and Rubinstein (1980) 

Literature on (dynamic) social network analysis

Rivera, Soderstrom and Uzzi (2010)
Lerner, Indlekofer, Nick and Brandes (2013) 

Further Reading

Allison, Paul D. 1982
Discrete-time methods for the analysis of event histories
Sociological methodology 13(1):61–98

Allison, Paul D. 2014
Event History and Survival Analysis Second Edition
Los Angeles: SAGE Publications

Andersen, Per Kragh and Richard D Gill. 1982
Cox’s regression model for counting processes: a large sample study
The Annals of Statistics 10:1100–1120

Box-Steffensmeier, Janet M and Bradford S Jones. 2004
Event history modeling: A guide for social scientists
Cambridge: Cambridge University Press

Brandenberger, Laurence. 2018
Trading Favors – Examining the Temporal Dynamics of Reciprocity in Congressional Collaborations Using Relational Event Models
Social Networks 54:238–253

Brandenberger, Laurence. Forthcoming
Predicting Network Events to Assess Goodness of Fit of Relational Event Models
Political Analysis Accepted for publication

Butts, Carter T. 2008
A relational event framework for social action
Sociological Methodology 38(1):155–200

Cox, David Roxbee and David Oakes. 1984
Analysis of survival data
London: Chapman and Hall

De Nooy, Wouter and Jan Kleinnijenhuis. 2013
Polarization in the media during an election campaign: A dynamic network model predicting support and attack among political actors
Political Communication 30(1):117–138

DuBois, Christopher, Carter Butts and Padhraic Smyth. 2013
Stochastic blockmodeling of relational event dynamics
In Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics. pp. 238–246

Gail, Michell H., Jay H. Lubin and Lawrence V. Rubinstein. 1980
Likelihood Calculations for Matched Case-Control Studies and Survival Studies with Tied Death Times
Biometrika 68:703–707

Kitts, James A, Alessandro Lomi, Daniele Mascia, Francesca Pallotti, Eric Quintane et al. 2016
Investigating the temporal dynamics of inter-organizational exchange: Patient transfers among Italian hospitals
American Journal of Sociology 123(3):850–910

Leenders, Roger Th AJ, Noshir S Contractor and Leslie A DeChurch. 2016
Once upon a time: Understanding team processes as relational event networks
Organizational Psychology Review 6(1):92–115

Leifeld, Philip and Laurence Brandenberger. 2019
Endogenous Coalition Formation in Policy Debates

Lerner, Jürgen, Margit Bussmann, Tom AB Snijders and Ulrik Brandes. 2013
Modeling frequency and type of interaction in event networks
Corvinus Journal of Sociology and Social Policy 4(1):3–32

Lerner, Jürgen, Natalie Indlekofer, Bobo Nick and Ulrik Brandes. 2013
Conditional independence in dynamic networks
Journal of Mathematical Psychology 57(6):275–283

Liang, Hai. 2014
The organizational principles of online political discussion: A relational event stream model for analysis of web forum deliberation
Human Communication Research 40(4):483–507

Malang, Thomas, Laurence Brandenberger and Philip Leifeld. 2018
Networks and Social Influence in European Legislative Politics
British Journal of Political Science

Patison, KP, E Quintane, DL Swain, G Robins and P Pattison. 2015
Time is of the essence: an application of a relational event model for animal social networks
Behavioral ecology and sociobiology 69(5):841–855

Pilny, Andrew, Aaron Schecter, Marshall Scott Poole and Noshir Contractor. 2016
An illustration of the relational event model to analyze group interaction processes
Group Dynamics: Theory, Research, and Practice 20(3):181

Pilny, Andrew, Jeffrey D Proulx, Ly Dinh and Ann L Bryan. 2017
An Adapted Structurational Framework for the Emergence of Communication Networks
Communication Studies 68(1):72–94

Quintane, Eric and Gianluca Carnabuci. 2016
How Do Brokers Broker? Tertius Gaudens, Tertius Iungens, and the Temporality of Structural Holes
Organization Science 27(6):1343– 1360

Quintane, Eric, Guido Conaldi, Marco Tonellato and Alessandro Lomi. 2014
Modeling Relational Events. A Case Study on an Open Source Software Project
Organizational Research Methods 17(1):23–50

Quintane, Eric, Philippa E Pattison, Garry L Robins and Joeri M Mol. 2013
Short- and long-term stability in organizational networks: Temporal structures of project teams
Social Networks 35(4):528–540

Rivera, Mark T, Sara B Soderstrom and Brian Uzzi. 2010
Dynamics of dyads in social networks: Assortative, relational, and proximity mechanisms
Annual Review of Sociology 36:91–115

Stadtfeld, Christoph and Andreas Geyer-Schulz. 2011
Analyzing event stream dynamics in two-mode networks: An exploratory analysis of private communication in a question and answer community
Social Networks 33(4):258–272

Tranmer, Mark, Christopher Steven Marcum, F Blake Morton, Darren P Croft and Selvino R de Kort. 2015
Using the relational event model (REM) to investigate the temporal dynamics of animal social networks
Animal Behaviour 101:99–105

Vu, Duy Q., David Hunter, Padhraic Smyth and Arthur U. Asuncion. 2011
Continuous-Time Regression Models for Longitudinal Networks
In Advances in Neural Information Processing Systems 24, ed. J. Shawe-Taylor, R. S. Zemel, P. L. Bartlett, F. Pereira and K. Q. Weinberger. Curran Associates, Inc. pp. 2492–2500

Welbers, Kasper and Wouter de Nooy. 2014
Stylistic accommodation on an internet forum as bonding: Do posters adapt to the style of their peers?
American Behavioral Scientist 58(10):1361–1375

Xia, Donggeng, Shawn Mankad and George Michailidis. 2016
Measuring Influence of Users in Twitter Ecosystems Using a Counting Process Modeling Framework
Technometrics 58(3):360–370

Zenk, Lukas and Christoph Stadtfeld. 2010
Dynamic organizations. How to measure evolution and change in organizations by analyzing email communication networks
Procedia – Social and Behavioral Sciences 4:14–25

The following other ECPR Methods School courses could be useful in combination with this one in a ‘training track .
Recommended Courses Before

Winter School

Introduction to Applied Social Network Analysis

Inferential Network Analysis

Introduction to R

Additional Information


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 in due time.

Note from the Academic Convenors

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, contact the instructor before registering.

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