1. Animal social network analysis (SNA) often uses proximity data obtained from automated tracking of individuals. Identifying associations based on proximity requires deciding on quantitative criteria such as the maximum distance or the longest time interval between visits of different individuals to still consider them associated. These quantitative criteria are not easily chosen based on a priori biological arguments alone.
2. Here we propose a procedure for optimizing proximity‐based association criteria in SNA, whereby different spatial and temporal criteria are screened to determine which combination detects more network structure. If we assume that biologically relevant associations among individuals are non‐random, and that proximity data are mostly influenced by those associations, then it is logical to select criteria that minimise random associations and show the underlying network structure more clearly.
3. We first used simulations to evaluate which of four simple descriptors of network structure remain unbiased (i.e. do not change directionally) when reducing the number of observations, since unbiased descriptors are necessary for comparing the structure of networks using different association criteria. Then, using two of those descriptors (coefficient of variation of the strength of associations and network entropy) and empirical proximity data from automated tracking of common waxbills Estrilda astrild in a mesocosm environment, we found that the structure‐based optimization procedure selected the most biologically relevant combination of spatial and temporal proximity criteria, in the sense that those criteria were also the best at distinguishing between previously known social subgroups of individuals.
4. These results indicate that, provided that the assumptions for structure‐based optimization are met, this procedure can find the most biologically relevant association criteria. Thus, under the condition that proximity data are shaped by non‐random social associations, and if using adequate descriptors of network structure, structure‐based optimization may be a useful tool for SNA, particularly when a priori biological arguments are insufficient to inform the choice of proximity‐based association criteria.

中文翻译：

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网络结构和基于邻近关系的关联标准的优化

1. 动物社交网络分析（SNA）通常使用从自动跟踪个体获得的邻近数据。根据接近度识别关联需要确定定量标准，例如最大距离或不同个体访视之间的最长时间间隔，以仍然将它们视为关联。仅基于先验生物学论据就不容易选择这些定量标准。
2. 在这里，我们提出了一种优化SNA中基于接近度的关联标准的过程，从而筛选出不同的时空标准，以确定哪种组合可以检测更多的网络结构。如果我们假设个体之间生物学上相关的关联是非随机的，并且邻近性数据主要受这些关联的影响，那么选择最小化随机关联并更清楚地显示底层网络结构的标准是合乎逻辑的。
3. 我们首先使用仿真来评估减少观察次数时网络结构的四个简单描述符中的哪个保持无偏（即，不发生方向性变化），因为使用不同关联标准比较网络结构时，必须使用无偏的描述符。然后，使用其中的两个描述符（关联强度和网络熵的变化系数）和在中观环境中自动跟踪普通蜡嘴鸟Estrilda astrild的经验接近数据，我们发现基于结构的优化程序选择了最生物学的方法在某种意义上说，这些标准也是最能区分先前已知的个体社会子群体的意义上，它们是空间和时间接近性标准的相关组合。
4. 这些结果表明，只要满足基于结构的优化的假设，该程序就可以找到最生物学相关的关联标准。因此，在邻近数据是由非随机社会关联构成的条件下，如果使用适当的网络结构描述符，基于结构的优化可能是SNA的有用工具，尤其是在先验生物学论证不足以告知选择时基于接近度的关联标准。