Social scientists have long appreciated that relationships between individuals cannot be described from observing a single domain, and that the structure across domains of interaction can have important effects on outcomes of interest (e.g., cooperation; Durkheim, 1893). One debate explicitly about this surrounds food sharing. Some argue that failing to find reciprocal food sharing means that some process other than reciprocity must be occurring, whereas others argue for models that allow reciprocity to span domains in the form of trade (Kaplan and Hill, 1985.). Multilayer networks, high‐dimensional networks that allow us to consider multiple sets of relationships at the same time, are ubiquitous and have consequences, so processes giving rise to them are important social phenomena. The analysis of multi‐dimensional social networks has recently garnered the attention of the network science community (Kivelä et al., 2014). Recent models of these processes show how ignoring layer interdependencies can lead one to miss why a layer formed the way it did, and/or draw erroneous conclusions (Górski et al., 2018). Understanding the structuring processes that underlie multiplex networks will help understand increasingly rich data sets, giving more accurate and complete pictures of social interactions.

中文翻译：

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为什么了解多元化的社交网络构建过程将有助于我们更好地了解人类行为的演变。

社会科学家早就意识到，不能通过观察单个领域来描述个体之间的关系，并且相互作用的跨领域结构可能会对感兴趣的结果产生重要影响（例如，合作； Durkheim，1893）。关于此的明确辩论围绕食物共享。一些人认为未能找到互惠的食物共享意味着必须进行互惠以外的其他过程，而另一些人则主张允许互惠以贸易形式跨越领域的模型（Kaplan and Hill，1985）。多层网络是使我们能够同时考虑多组关系的高维度网络，这些关系无处不在并且会产生后果，因此产生这些关系的过程是重要的社会现象。多维社交网络的分析最近引起了网络科学界的关注（Kivelä等，2014）。这些过程的最新模型表明，忽略层之间的相互依赖性如何导致人们错过层为什么形成层的方式和/或得出错误的结论（Górskiet al。，2018）。了解多重网络基础的结构过程将有助于理解日益丰富的数据集，从而提供更加准确和完整的社交互动图景。