  Over the past decade network theory has been applied successfully to the
study of a variety of complex adaptive systems. However, the application of
these techniques to non-human social networks has several shortfalls. Firstly,
in most cases the strength of associations between individuals is disregarded.
Secondly, present techniques assume that observed interactions are invariant
values and not statistical samples taken from a population. These two
simplifications have weakened the value of these techniques when applied to the
study of animal social systems. Here we introduce a set of behaviorally
meaningful weighted network statistics that can be readily applied to matrices
of association indices between pairs of individual animals. We also introduce
bootstrapping techniques that estimate the effects of sampling uncertainty on
the network statistics and structure. Finally, we discuss the use of
randomisation tests to detect the departure of observed network statistics from
expected values under null hypotheses of random association given the sampling
structure of the data. We use two case studies to show that these techniques
provide invaluable insight in the dynamics of interactions within social units
and in the community structure of societies.
