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Inferential Network Analysis (RSS2.06) - Closed

The course will introduce statistical models for network data and other data with complex dependence among observations. A range of models will be discussed theoretically, in application and using R.

Duration: one-week.



    This course is closed, registration is no longer possible. 

    Inferential Network Analysis introduces statistical methods for analysing networks. Networks, or graphs, are sets of nodes and their connecting ties. Networks are used to model a wide range of complex social and political phenomena, such as policy networks among political actors around the design of new regulation; lobbying ties of interest groups to policy makers or lobbying coalitions; recruitment of experts into international organizations; patronage relationships; multilevel or collaborative governance systems; international relations, including conflict, alliances, trade, and migration; financial deals between organizations; political debates among actors about policies; or the diffusion of policies among states. Systems like these usually evolve over time in complex ways, and as researchers we want to understand the formation of ties between nodes at the micro level in order to understand how the system evolves. We also want to understand the adoption of behavior as a consequence of being embedded in a network. 

    The central questions are: 

    • How can we model any data where the observations are not independent and identically distributed (i.i.d.)? 
    • How can we explain and model connections between nodes (or characteristics of nodes) using covariate data and theories about the endogeneity in the data? 
    • And how can we simulate such processes forward in time to predict future states of the network or the characteristics of the nodes? 

    The course “Inferential Network Analysis” introduces a range of statistical models for explaining and predicting the formation of ties in networks using characteristics of the nodes, their ties, and the network. You will consider the exponential random graph model (ERGM) as the workhorse model of statistical network analysis in the first half of the course, including specification, estimation, and implementation in R. 
    You will then discuss extensions to temporal and valued relations before you consider alternative modeling choices, including the family of latent space models, the quadratic assignment procedure, the stochastic actor-oriented model, and the relational event model. Finally, you will also learn about the use of network autocorrelation models to explain the state or behavior of a node by considering the node’s network embeddedness. All models will be discussed theoretically, in application, and practically using R. Participants will be given daily assignments to solve after class to maximize the learning experience.


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    Starting date

    24 June 2024, 9 am
    Educational method
    Main Language
    24 June 2024, 9 am - 28 June 2024, 5 pm
    Philip Leifeld
    Unique code


    Type of education
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    See the requirements in cost and admission
    Study load (ECTS)
    Radboud Summer School

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    Philip Leifeld

    Philip Leifeld

    Philip Leifeld is a professor in the Department of Social Statistics at the University of Manchester (starting in April 2024) and in the Department of Government at the University of Essex (until March 2024). Professor Leifeld's research tries to understand the formation of networks between political actors in the pursuit of policy goals. He develops and implements statistical methods for the analysis of complex networks. Professor Leifeld's work has been published in the American Journal of Political Science and other leading political science journals but also technical outlets such as Physica A: Statistical Mechanics and the Journal of Statistical Software.

    This course is closed, registration is no longer possible. 



    • Regular: €1049 (application deadline 1st of May)
    • Student & PhD's: €699 (application deadline 1st of May)

    Includes: your course, short morning and late afternoon courses, coffee and tea during breaks, a warm lunch every day, Official Opening, MethodsNET Café (including some drinks and snacks) Official Closing (with some drinks and snacks) and a 1-year (2024 calendar year) free membership as MethodsNET regular member.

    Excludes: transport, accommodation, social events and other costs. 

    Discounts and Scholarships


    Level of participant: 

    • Master
    • PhD
    • Postdoc
    • Professional

    Admission requirements: Participants should have a working knowledge of logistic regression before participating in the course, and they will be expected to solve tasks in R. While this is not a course about R, students would hence benefit most from having a working knowledge of R before joining the course.

    Admission documents: None