The building blocks of social structure: simulating constraints of social network analysis for inference about group-level properties

Social Network Analysis (SNA) is an interdisciplinary field that can study social structures from various angles, from evolutionary pressures to the emergence of group-level structures. However, these SNA metrics are often disconnected from the factors that shape them, such as individual dispositions, ecological constraints, and group-level affordances.

To address this issue, researchers developed a model to simulate fission-fusion spatial association data using specific parameters. They divided animal social systems into three components: individual dispositions, external factors, and the social structure that emerges. Each component was represented by one or more model parameters, which together describe the processes leading to different observable features of animal social structure, such as clustering coefficient, density, and modularity, within an SNA framework.

By doing so, the model directly links global network metrics to the underlying behavioral processes that generate them. The researchers found that subtle changes to factors like group size, foraging party size and variability, individual behavior variation, observation bias, imposed group sub-structures, and observation effort can affect all network metrics non-linearly and in some cases non-monotonically.

The study highlights that overlooking these underlying behavioral processes can lead to biased inferences drawn from SNA. The model and conceptual framework presented in this research provide a novel approach to "opening the black box" of SNA and systematically addressing the evolution of diverse group-level social structures.