  The importance of structured, complex connectivity patterns found in several
real-world systems is to a great extent related to their respective effects in
constraining and even defining the respective dynamics. Yet, while complex
networks have been comprehensively investigated along the last decade in terms
of their topological measurements, relatively less attention has been focused
on the characterization of the respective dynamics. Introduced recently, the
diversity entropy of complex systems can provide valuable information about the
respective possible unfolding of dynamics. In the case of self-avoiding random
walks, the situation assumed here, the diversity measurement allows one to
quantify in how many different places an agent may effectively arrive after a
given number of steps from its initial activity. Because this measurement is
highly affected by border effects frequently found as a consequence of network
sampling, it becomes critical to devise means for sound estimation of the
diversity without being affected by this type of artifacts. We describe such an
algorithm and illustrate its potential with respect to the characterization of
the self-avoiding random walk dynamics in two real-world networks, namely bone
canals and air transportation.
