A framework to evaluate whether to pool or separate behaviors in a multilayer network

TL;DR

This paper introduces a framework for deciding whether to pool or separate behaviors in multilayer networks, demonstrated with monk parakeet social data, to improve analysis accuracy and interpretability.

Contribution

The authors develop a novel framework to evaluate behavior pooling decisions in multilayer networks, accounting for data properties and informing better analytical choices.

Findings

Pooling behaviors can be justified when data sparsity is controlled.

Different behaviors may convey distinct social information, suggesting separation in some cases.

The framework is adaptable to various behaviors and research questions.

Abstract

A multilayer network approach combines different network layers, which are connected by interlayer edges, to create a single mathematical object. These networks can contain a variety of information types and represent different aspects of a system. However, the process for selecting which information to include is not always straightforward. Using data on two agonistic behaviors in a captive population of monk parakeets (Myiopsitta monachus), we developed a framework for investigating how pooling or splitting behaviors at the scale of dyadic relationships (between two individuals) affects individual- and group-level social properties. We designed two reference models to test whether randomizing the number of interactions across behavior types results in similar structural patterns as the observed data. Although the behaviors were correlated, the first reference model suggests that the two behaviors convey different information about some social properties and should therefore not be pooled. However, once we controlled for data sparsity, we found that the observed measures corresponded with those from the second reference model. Hence, our initial result may have been due to the unequal frequencies of each behavior. Overall, our findings support pooling the two behaviors. Awareness of how selected measurements can be affected by data properties is warranted, but nonetheless our framework disentangles these efforts and as a result can be used for myriad types of behaviors and questions. This framework will help researchers make informed and data-driven decisions about which behaviors to pool or separate, prior to using the data in subsequent multilayer network analyses.