Analysing Networks of Networks

TL;DR

This paper introduces a method to analyze complex multilevel networks by transforming multiple interconnected networks into a unified multilevel structure using line graphs, enabling advanced modeling of social and report ties.

Contribution

It proposes a novel approach to jointly represent and analyze layered networks through line graph transformations, facilitating the study of social-report associations.

Findings

Method successfully applied to flood management groups

Enables analysis with multilevel exponential random graph models

Flexible framework compatible with various multilevel network models

Abstract

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.