  Characterization of real-world complex systems increasingly involves the
study of their topological structure using graph theory. Among global network
properties, small-world property, consisting in existence of relatively short
paths together with high clustering of the network, is one of the most
discussed and studied. When dealing with coupled dynamical systems, links among
units of the system are commonly quantified by a measure of pairwise
statistical dependence of observed time series (functional connectivity). We
argue that the functional connectivity approach leads to upwardly biased
estimates of small-world characteristics (with respect to commonly used random
graph models) due to partial transitivity of the accepted functional
connectivity measures such as the correlation coefficient. In particular, this
may lead to observation of small-world characteristics in connectivity graphs
estimated from generic randomly connected dynamical systems. The ubiquity and
robustness of the phenomenon is documented by an extensive parameter study of
its manifestation in a multivariate linear autoregressive process, with
discussion of the potential relevance for nonlinear processes and measures.
