Constructing, conducting and interpreting animal social network analysis.
Constructing, conducting and interpreting animal social network analysis.
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DOI：
10.1111/1365-2656.12418
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发表时间：
2015-09
期刊：
影响因子：
--
通讯作者：
Whitehead H
中科院分区：
文献类型：
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作者：
Farine DR;Whitehead H
动物社会网络是对社会结构的描述，除了它们对理解社会性的内在兴趣外，还可以对生物学的许多领域产生重大影响。网络分析提供了一个灵活的工具箱，用于检验广泛的假设，并以定量和可比较的方式描述物种或种群的社会系统。然而，它需要仔细考虑潜在的假设，特别是区分真实网络和观察到的网络，并控制社交数据中常见的固有偏差。我们为使用这个框架分析动物社会系统和检验假说提供了一个实用指南。首先，我们讨论在定义节点和边以及设计收集数据的方法时的关键注意事项。我们讨论了从这些数据推断和显示社交网络的不同方法。然后，我们概述了用于量化节点和网络的性质的方法，以及用于测试关于网络结构和网络过程的假设的方法。最后，我们提供了有关评估被观测网络的能力和准确性的信息。除了这份手稿，我们还提供了附录，其中包含有关常见编程例程的背景信息，以及如何使用r编程语言执行网络分析的实例。最后，我们讨论了社交网络分析目前面临的一些主要挑战和有趣的未来发展方向。特别是，我们强调了在社交网络上进行实验性操作来解决研究问题的潜力没有得到充分开发。
Animal social networks are descriptions of social structure which, aside from their intrinsic interest for understanding sociality, can have significant bearing across many fields of biology. Network analysis provides a flexible toolbox for testing a broad range of hypotheses, and for describing the social system of species or populations in a quantitative and comparable manner. However, it requires careful consideration of underlying assumptions, in particular differentiating real from observed networks and controlling for inherent biases that are common in social data. We provide a practical guide for using this framework to analyse animal social systems and test hypotheses. First, we discuss key considerations when defining nodes and edges, and when designing methods for collecting data. We discuss different approaches for inferring social networks from these data and displaying them. We then provide an overview of methods for quantifying properties of nodes and networks, as well as for testing hypotheses concerning network structure and network processes. Finally, we provide information about assessing the power and accuracy of an observed network. Alongside this manuscript, we provide appendices containing background information on common programming routines and worked examples of how to perform network analysis using the r programming language. We conclude by discussing some of the major current challenges in social network analysis and interesting future directions. In particular, we highlight the under‐exploited potential of experimental manipulations on social networks to address research questions.