Here, we propose an adaptive random walk (ARW), which, differently from the maximum entropy random walk, maximizes dispersion by updating its transition rule on the local information collected while exploring the network. The ARW is obtained via a large-deviation representation of the MERW as a finite-time rare event of an unbiased random walk and study its dynamics on synthetic and real-world networks.
