title: Smart random walkers: the cost of knowing the path.
abstract: In this work we study the problem of targeting signals in networks using entropy information measurements to quantify the cost of targeting. We introduce a penalization rule that imposes a restriction on the long paths and therefore focuses the signal to the target. By this scheme we go continuously from fully random walkers to walkers biased to the target. We found that the optimal degree of penalization is mainly determined by the topology of the network. By analyzing several examples, we have found that a small amount of penalization reduces considerably the typical walk length, and from this we conclude that a network can be efficiently navigated with restricted information.
authors.affiliation: Facultad de Matemática, Astronomía y Física, Universidad Nacional de Córdoba and Instituto de Física Enrique Gaviola, IFEG-CONICET, Ciudad Universitaria, 5000 Córdoba, Argentina. perotti@famaf.unc.edu.ar
authors.forename: Juan I
authors.initials: JI
authors.lastname: Perotti
authors.forename: Orlando V
authors.initials: OV
authors.lastname: Billoni
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D015233:Models, Statistical