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 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 sociality measures derived from observed data fell outside the distribution of those derived from the reference model. However, once we controlled for data sparsity in our second reference model, we found that measures from the observed data then fell within the range of those from the reference model which showed that this result may have been due to the unequal frequencies of each observed behavior. Thus, our findings support pooling the two behaviors. This framework can be used for any type of behavior and question, however, caution should be used when interpreting the results as some measures are sensitive to data properties, such as unequal rates of observed behavior in our case. 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.

中文翻译：

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评估是否在多层网络中合并或分离行为的框架

多层网络方法结合了通过层间边缘连接的不同网络层，以创建单个数学对象。这些网络可以包含各种信息类型并代表系统的不同方面。然而，选择要包含哪些信息的过程并不总是那么简单。利用圈养僧侣鹦鹉（Myiopsitta monachus）种群中两种竞争行为的数据，我们开发了一个框架，用于研究二元关系（两个个体之间）规模的集中或分裂行为如何影响群体层面的社会属性。我们设计了两个参考模型来测试跨行为类型的交互数量的随机化是否会产生与观察数据相似的结构模式。尽管这些行为是相关的，但从观察数据中得出的社交性度量超出了从参考模型中得出的分布。然而，一旦我们控制了第二个参考模型中的数据稀疏性，我们发现观察到的数据的测量值落在参考模型的测量值范围内，这表明该结果可能是由于每个观察到的行为的频率不相等造成的。因此，我们的研究结果支持将这两种行为结合起来。该框架可用于任何类型的行为和问题，但是，在解释结果时应小心谨慎，因为某些度量对数据属性敏感，例如在我们的案例中观察到的行为的比率不同。该框架将帮助研究人员在随后的多层网络分析中使用数据之前，就合并或分离哪些行为做出明智的数据驱动决策。