Network structure and the optimisation of proximity-based association criteria

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.
Here we propose a procedure for optimising 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.
We first used simulations to evaluate which of four simple descriptors...