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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
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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