  Recently, some studies started to unveil the wealthy of interactions that
occur between groups of nodes when looking at the small scale of interactions
taking place in complex networks. Such findings claim for a new systematic
methodology to quantify, at node level, how a dynamics is being influenced (or
differentiated) by the structure of the underlying system. Here we define a new
measure that, based on dynamical characteristics obtained for a large set of
initial conditions, compares the dynamical behavior of the nodes present in the
system. Through this measure we find that the geographic and Barab\'asi-Albert
models have high capacity for generating networks that exhibit groups of nodes
with distinct dynamics compared to the rest of the network. The application of
our methodology is illustrated with respect of two real systems. In the first
we use the neuronal network of the nematode Caenorhabditis elegans to show that
the interneurons of the ventral cord of the nematode presents a very large
dynamical differentiation when compared to the rest of the network. The second
application concerns the SIS epidemic model on an airport network, where we
quantify how different the distribution of infection times of high and low
degree nodes can be, when compared to the expected value for the network.
