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

Autor:	Cui, Kai, KhudaBukhsh, Wasiur R., Koeppl, Heinz
Rok vydání:	2022
Předmět:	Physics - Physics and Society Condensed Matter - Disordered Systems and Neural Networks Condensed Matter - Statistical Mechanics
Druh dokumentu:	Working Paper
Popis:	Interacting particles on graphs are routinely used to study magnetic behaviour in physics, disease spread in epidemiology, and opinion dynamics in social sciences. The literature on mean-field approximations of such systems for large graphs is limited to 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. Comment: v2: Added references; adjusted length. v3: Full-length references
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2201.04999 Zobrazit plný text záznamu View this record from Arxiv