Abstract

Social network analysis is a suite of approaches for exploring relational data. Two approaches commonly used to analyze animal social network data are permutation-based tests of significance and exponential random graph models. However, the performance of these approaches when analyzing different types of network data has not been simultaneously evaluated. Here we test both approaches to determine their performance when analyzing a range of biologically realistic simulated animal social networks. We examined the false positive and false negative error rate of an effect of a two-level explanatory variable (e.g., sex) on the number and combined strength of an individual’s network connections. We measured error rates for two types of simulated data collection methods in a range of network structures, and with/without a confounding effect and missing observations. Both methods performed consistently well in networks of dyadic interactions, and worse on networks constructed using observations of individuals in groups. Exponential random graph models had a marginally lower rate of false positives than permutations in most cases. Phenotypic assortativity had a large influence on the false positive rate, and a smaller effect on the false negative rate for both methods in all network types. Aspects of within- and between-group network structure influenced error rates, but not to the same extent. In "grouping event-based" networks, increased sampling effort marginally decreased rates of false negatives, but increased rates of false positives for both analysis methods. These results provide guidelines for biologists analyzing and interpreting their own network data using these methods.

中文翻译：

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分析动物网络时置换和指数随机图模型的性能

摘要

社交网络分析是用于探索关系数据的一套方法。通常用于分析动物社交网络数据的两种方法是基于排列的显着性检验和指数随机图模型。但是，尚未同时评估这些方法在分析不同类型的网络数据时的性能。在这里，我们分析两种生物学上现实的模拟动物社交网络时，测试这两种方法以确定它们的性能。我们检查了两级解释变量（例如，性别）对个人网络连接的数量和综合强度的影响的误报率和误报率。我们测量了一系列网络结构中两种类型的模拟数据收集方法的错误率，并且具有/没有混淆效果和缺少观察结果。两种方法在二元交互网络中始终表现良好，而在使用组中个人观察结果构建的网络中则表现较差。在大多数情况下，指数随机图模型的假阳性率略低于排列。在所有网络类型中，这两种方法的表型分类对假阳性率的影响较大，而对假阴性率的影响较小。组内和组间网络结构的各个方面都会影响错误率，但程度不同。在“基于事件的分组”网络中，增加的采样工作量在一定程度上降低了假阴性的发生率，但是对于两种分析方法而言，假阳性的发生率却有所增加。