Social network dynamics: the importance of distinguishing between heterogeneous and homogeneous changes.
Social network analysis is increasingly applied to understand the evolution of animal sociality. Identifying ecological and evolutionary drivers of complex social structures requires inferring how social networks change over time. In most observational studies, sampling errors may affect the apparent network structures.Here, we argue that existing approaches tend not to control sufficiently for some types of sampling errors when social networks change over time. Specifically, we argue that two different types of changes may occur in social networks, heterogeneous and homogeneous changes, and that understanding network dynamics requires distinguishing between these two different types of changes, which are not mutually exclusive. Heterogeneous changes occur if relationships change differentially, e.g. if some relationships are terminated but others remain intact. Homogeneous changes occur if all relationships are proportionally affected in the same way, e.g. if grooming rates decline similarly across all dyads. Homogeneous declines in the strength of relationships can strongly reduce the probability of observing weak relationships, producing the appearance of heterogeneous network changes. Using simulations, we confirm that failing to differentiate homogeneous and heterogeneous changes can potentially lead to false conclusions about network dynamics. We also show that bootstrap tests fail to distinguish between homogeneous and heterogeneous changes. As a solution to this problem we show that an appropriate randomization test can infer whether heterogeneous changes occurred. Finally, we illustrate the utility of using the randomization test by performing an example analysis using an empirical data set on wild baboons.
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