The formalization of multilayer networks allows for new ways to measure sociality in complex social systems, including groups of animals. The same mathematical representation and methods are widely applicable across fields and study systems, and a network can represent drastically different types of data. As such, in order to apply analyses and interpret the results in a meaningful way the researcher must have a deep understanding of what their network is representing and what parts of it are being measured by a given analysis. Multilayer social networks can represent social structure with more detail than is often present in single layer networks, including multiple “types” of individuals, interactions, or relationships, and the extent to which these types are interdependent. Multilayer networks can also encompass a wider range of social scales, which can help overcome complications that are inherent to measuring sociality. In this paper, I dissect multilayer networks into the parts that correspond to different components of social structures. I then discuss common pitfalls to avoid across different stages of multilayer network analyses—some novel and some that always exist in social network analysis but are magnified in multi-layer representations. This paper serves as a primer for building a customized toolkit of multilayer network analyses, to probe components of social structure in animal social systems.

中文翻译：

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多层网络分析作为测量社会结构的工具包


多层网络的形式化提供了衡量复杂社会系统（包括动物群体）中社会性的新方法。相同的数学表示和方法广泛适用于各个领域和研究系统，并且网络可以表示截然不同类型的数据。因此，为了以有意义的方式应用分析和解释结果，研究人员必须深入了解他们的网络所代表的内容以及给定分析正在测量网络的哪些部分。多层社交网络可以比单层网络中更详细地表示社会结构，包括多种“类型”的个体、交互或关系，以及这些类型相互依赖的程度。多层网络还可以涵盖更广泛的社会规模，这有助于克服衡量社会性所固有的复杂性。在本文中，我将多层网络剖析成与社会结构的不同组成部分相对应的部分。然后，我讨论了多层网络分析不同阶段需要避免的常见陷阱，其中一些是新颖的，而另一些则始终存在于社交网络分析中，但在多层表示中被放大。本文作为构建多层网络分析的定制工具包的入门读本，以探讨动物社会系统中社会结构的组成部分。