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Dear colleagues,

As a followup to the session at Sunbelt XXXIX in June, we are organizing a second session on Inference and Generalisability in Modelling Samples of Networks and Multi-Level Network Data at the EUSN2019 conference, held in Zurich, September 9-12.

The goal of this session is to bring attention to methodological issues related to generalizability or inference to populations of networks and to propose methods and diagnostics for joint estimation of models for multiple networks or for networks with multi-level structure. A further motivation is given below as a well as a number of relevant questions.
We welcome contributions on any of these, or related questions, or applications in which generalizability or inference to populations of networks play a role.

Abstracts for the session can be submitted until April 12 on the conference website or on .

Please do no hesitate to contact us with further questions.

Marijtje van Duijn and Pavel Krivitsky

Sociometric data that we collect are increasingly rich, and we increasingly analyse not single networks but ensembles of networks. Data using the same name generator on disjoint sets of actors in disjoint but similar settings have been collected about classrooms, schools, households, firms, legislative bodies, and other such replicable scenarios.
Given such data, we often wish to pool the information from these multiple networks, and to draw conclusions generalisable to a broader population of networks in those settings. Methods to do so range from post-hoc meta-analyses to full hierarchical multi-level models.

These joint analyses raise a number of methodological questions, however. Some of them are questions that are asked in any situation that involves sampling from a population:
* What does it mean to draw a representative sample of networks?
* Can networks selected using different procedures be analysed together, and how?
* What "population" quantities are actually being estimated when metanalyses are performed or multilevel models fit?

Others are specific to social networks:
* Can the same model be fit to all of the networks in the ensemble?
* How can parameter estimates from networks that vary in size and/or composition be compared?

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