Maximal dispersion of adaptive random walks

Autor:	Di Bona, Gabriele, Di Gaetano, Leonardo, Latora, Vito, Coghi, Francesco
Rok vydání:	2022
Předmět:	Condensed Matter - Statistical Mechanics Condensed Matter - Disordered Systems and Neural Networks
Zdroj:	Physical Review Research 4, L042051 (2022)
Druh dokumentu:	Working Paper
DOI:	10.1103/PhysRevResearch.4.L042051
Popis:	Maximum entropy random walks (MERWs) are maximally dispersing and play a key role in optimizing information spreading in various contexts. However, building MERWs comes at the cost of knowing beforehand the global structure of the network, a requirement that makes them totally inadequate in real case scenarios. Here, we propose an adaptive random walk (ARW), which instead maximizes dispersion by updating its transition rule on the local information collected while exploring the network. We show how to derive ARW via a large-deviation representation of MERW and study its dynamics on synthetic and real world networks. Comment: 20 pages, 12 figures, 1 table
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2202.13923 Zobrazit plný text záznamu View this record from Arxiv