  Interactive programming environments are powerful tools for promoting
innovative network thinking, teaching science of complexity, and exploring
emergent phenomena. This paper reports on our recent development of the
deterministic random walk model in NetLogo, a leading platform for
computational thinking, eco-system thinking, and multi-agent cross-platform
programming environment. The deterministic random walk is foundational to
modeling dynamical processes on complex networks. Inspired by the temporal
visualizations offered in NetLogo, we investigated the relationship between
network topology and diffusion saturation time for the deterministic random
walk model. Our analysis uncovers that in Erd\H{o}s-R\'{e}nyi graphs, the
saturation time exhibits an asymmetric pattern with a considerable probability
of occurrence. This behavior occurs when the hubs, defined as nodes with
relatively higher number of connections, emerge in Erd\H{o}s-R\'{e}nyi graphs.
Yet, our analysis yields that the hubs in Barab\'{a}si-Albert model stabilize
the the convergence time of the deterministic random walk model. These findings
strongly suggest that depending on the dynamical process running on complex
networks, complementing characteristics other than the degree need to be taken
into account for considering a node as a hub. We have made our development
open-source, available to the public at no cost at
https://github.com/bravandi/NetLogo-Dynamical-Processes.
