Motif-based mean-field approximation of interacting particles on clustered networks.

Cui, Kai; KhudaBukhsh, Wasiur R; Koeppl, Heinz

Cui, Kai; KhudaBukhsh, Wasiur R; Koeppl, Heinz.

Afiliación

Cui K; Department of Electrical Engineering and Information Technology, Technische Universität Darmstadt, 64287 Darmstadt, Germany.
KhudaBukhsh WR; University of Nottingham, Nottingham, United Kingdom.
Koeppl H; Department of Electrical Engineering and Information Technology, Technische Universität Darmstadt, 64287 Darmstadt, Germany.

Phys Rev E ; 105(4): L042301, 2022 Apr.

Article en En | MEDLINE | ID: mdl-35590665

RESUMEN

Interacting particles on graphs are routinely used to study magnetic behavior in physics, disease spread in epidemiology, and opinion dynamics in social sciences. The literature on mean-field approximations of such systems for large graphs typically remains limited to specific dynamics, or assumes cluster-free graphs for which standard approximations based on degrees and pairs are often reasonably accurate. Here, we propose a motif-based mean-field approximation that considers higher-order subgraph structures in large clustered graphs. Numerically, our equations agree with stochastic simulations where existing methods fail.

Texto completo

Añadir a Mi BVS

Imprimir

XML

PubMed Links

Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Phys Rev E Año: 2022 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Estados Unidos

Texto completo

Añadir a Mi BVS

Imprimir

XML

PubMed Links

Buscar en Google