  We consider data with multiple observations or reports on a network in the
case when these networks themselves are connected through some form of network
ties. We could take the example of a cognitive social structure where there is
another type of tie connecting the actors that provide the reports; or the
study of interpersonal spillover effects from one cultural domain to another
facilitated by the social ties. Another example is when the individual semantic
structures are represented as semantic networks of a group of actors and
connected through these actors' social ties to constitute knowledge of a social
group. How to jointly represent the two types of networks is not trivial as the
layers and not the nodes of the layers of the reported networks are coupled
through a network on the reports. We propose to transform the different
multiple networks using line graphs, where actors are affiliated with ties
represented as nodes, and represent the totality of the different types of ties
as a multilevel network. This affords studying the associations between the
social network and the reports as well as the alignment of the reports to a
criterion graph. We illustrate how the procedure can be applied to studying the
social construction of knowledge in local flood management groups. Here we use
multilevel exponential random graph models but the representation also lends
itself to stochastic actor-oriented models, multilevel blockmodels, and any
model capable of handling multilevel networks.
