Finn Kelly R
Neukom Institute, Dartmouth College, Hanover, NH 03755, USA.
Curr Zool. 2021 Feb;67(1):81-99. doi: 10.1093/cz/zoaa079. Epub 2021 Jan 11.
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.
多层网络的形式化使得在包括动物群体在内的复杂社会系统中测量社会性有了新方法。相同的数学表示和方法在各个领域和研究系统中广泛适用，并且一个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并解释结果，研究人员必须深入理解他们的网络所代表的内容以及给定分析所测量的网络部分。多层社会网络能够比单层网络更详细地表示社会结构，包括个体、互动或关系的多种“类型”，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尺度，这有助于克服测量社会性时固有的复杂性。在本文中，我将多层网络剖析为与社会结构不同组成部分相对应的部分。然后我讨论了在多层网络分析的不同阶段要避免的常见陷阱——一些是新颖的，一些是社会网络分析中一直存在但在多层表示中被放大的。本文作为构建多层网络分析定制工具包的入门指南，用于探究动物社会系统中的社会结构组成部分。
