1. Animal social networks are often used to describe dynamic social systems, where individual behaviour generates network‐level structures that subsequently influence individual‐level behaviour. This interdependence between individual behaviour and group structuring is of central concern for questions concerning the evolution and development of social systems and collective animal behaviour more generally.
2. Various statistical methods exist for estimating network changes through time. One approach, time‐aggregated networks, takes repeated snapshots of interactions within windows of time to generate a time series of networks. However, there remain many analytical hurdles when implementing the time‐aggregated approach. To ameliorate this, we introduce an r package netTS that focuses on three analytical steps for analysing time‐aggregated networks: choosing appropriate time scale using bootstrapping, comparing patterns to relevant null models using permutation and finally building and interpreting statistical models using simulated data. We use simulated data to first highlight these steps, then use observed grooming data from a group of vervet monkeys as an applied example.
3. Our results suggest that the use of bootstrapping and permutation can accurately extract known patterns from simulated data. Using this approach with vervet data suggests that there is consistent social structuring, differing from what would be expected due to chance, and that some individuals are contributing to this structure more than others (i.e. keystone individuals).
4. We demonstrate that bootstrapping, permutation and simulation can aid in constructing and interpreting time‐aggregated networks. We suggest that the use of time‐aggregated networks to quantify patterns of network change can be a useful tool alongside process‐based approaches that seek mechanistic descriptions. Ultimately, by looking at both patterns and processes, dynamic networks can be used to better understand how individual behaviour generates social structures, and in turn how individual behaviour can be influenced by social structures, ultimately leading to a better understanding of the evolution of social behaviour.

中文翻译：

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构建和分析时间聚合网络：自举，置换和仿真的作用

1. 动物社交网络通常用于描述动态的社交系统，其中个人行为会生成网络级结构，这些结构随后会影响个人级行为。个体行为与群体结构之间的这种相互依存关系到社会系统和集体动物行为的演化与发展问题。
2. 存在各种用于估计网络随时间变化的统计方法。一种方法是时间聚合网络，它在时间窗口内对交互进行重复快照，以生成网络时间序列。但是，在实施时间汇总方法时，仍然存在许多分析障碍。为了改善这一点，我们介绍了一个r包网络TS，重点介绍了用于分析时间聚合网络的三个分析步骤：使用自举选择合适的时间标度，使用置换将模式与相关的空模型进行比较，最后使用模拟数据来构建和解释统计模型。我们使用模拟数据首先突出显示这些步骤，然后将观察到的来自一组黑长尾猴的修饰数据用作应用示例。
3. 我们的结果表明，使用自举和置换可以从模拟数据中准确提取已知模式。将这种方法与黑手党的数据结合使用，表明存在一致的社会结构，这与偶然性所预期的结果有所不同，并且某些人对这种结构的贡献要大于其他人（即基石个体）。
4. 我们证明自举，置换和仿真可以帮助构造和解释时间聚合网络。我们建议，使用时间聚合网络来量化网络变化的模式可以与寻求基于机械方法的基于过程的方法一起使用，是一种有用的工具。最终，通过观察模式和过程，可以使用动态网络更好地了解个人行为如何产生社会结构，进而可以通过社会结构影响个人行为，从而最终更好地理解社会行为的演变。 。