Social organisms often show collective behaviours such as group foraging or movement. Collective behaviours can emerge from interactions between group members and may depend on the behaviour of key individuals. When social interactions change over time, collective behaviours may change because these behaviours emerge from interactions among individuals. Despite the importance of, and growing interest in, the temporal dynamics of social interactions, it is not clear how to quantify changes in interactions over time or measure their stability. Furthermore, the temporal scale at which we should observe changes in social networks to detect biologically meaningful changes is not always apparent. Here we use multilayer network analysis to quantify temporal dynamics of social networks of the social spider Stegodyphus dumicola and determine how these dynamics relate to individual and group behaviours. We found that social interactions changed over time at a constant rate. Variation in both network structure and the identity of a keystone individual was not related to the mean or variance of the collective prey attack speed. Individuals that maintained a large and stable number of connections, despite changes in network structure, were the boldest individuals in the group. Therefore, social interactions and boldness are linked across time, but group collective behaviour is not influenced by the stability of the social network. Our work demonstrates that dynamic social networks can be modelled in a multilayer framework. This approach may reveal biologically important temporal changes to social structure in other systems.

中文翻译：

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使用多层网络分析探索集体行为的时间动态

社会生物经常表现出集体行为，例如集体觅食或运动。集体行为可以从群体成员之间的互动中产生，并且可能取决于关键个体的行为。当社会互动随着时间的推移而发生变化时，集体行为可能会发生变化，因为这些行为来自个人之间的互动。尽管社会互动的时间动态很重要，而且人们对此越来越感兴趣，但尚不清楚如何量化互动随时间的变化或衡量其稳定性。此外，我们应该观察社交网络变化以检测具有生物学意义的变化的时间尺度并不总是很明显。在这里，我们使用多层网络分析来量化社交蜘蛛 Stegodyphus dumicola 社交网络的时间动态，并确定这些动态与个人和群体行为的关系。我们发现社交互动会随着时间的推移以恒定的速度发生变化。网络结构和基石个体身份的变化与集体猎物攻击速度的均值或方差无关。尽管网络结构发生了变化，但保持大量稳定连接的个体是该组中最大胆的个体。因此，社交互动和大胆是跨时间联系的，但群体集体行为不受社交网络稳定性的影响。我们的工作表明，可以在多层框架中对动态社交网络进行建模。