2026-04-17T15:06:57Zhttps://recercat.cat/oai/request

oai:recercat.cat:2445/1196212025-12-04T20:42:00Zcom_2072_1057col_2072_478822col_2072_478917

00925njm 22002777a 4500 dc Prignano, Luce author Moreno, Yamir author Díaz Guilera, Albert author 2018-02-06T14:00:59Z 2018-02-06T14:00:59Z 2012-12-26 2018-02-06T14:00:59Z Finding efficient algorithms to explore large networks with the aim of recovering information about their structure is an open problem. Here, we investigate this challenge by proposing a model in which random walkers with previously assigned home nodes navigate through the network during a fixed amount of time. We consider that the exploration is successful if the walker gets the information gathered back home, otherwise no data are retrieved. Consequently, at each time step, the walkers, with some probability, have the choice to either go backward approaching their home or go farther away. We show that there is an optimal solution to this problem in terms of the average information retrieved and the degree of the home nodes and design an adaptive strategy based on the behavior of the random walker. Finally, we compare different strategies that emerge from the model in the context of network reconstruction. Our results could be useful for the discovery of unknown connections in large-scale networks. Xarxes complexes (Física) Complex networks (Physics) Exploring complex networks by means of adaptive walkers