Motif-based mean-field approximation of interacting particles on clustered networks
Physics and Society
2022-07-15 v3 Disordered Systems and Neural Networks
Statistical Mechanics
Abstract
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.
Keywords
Cite
@article{arxiv.2201.04999,
title = {Motif-based mean-field approximation of interacting particles on clustered networks},
author = {Kai Cui and Wasiur R. KhudaBukhsh and Heinz Koeppl},
journal= {arXiv preprint arXiv:2201.04999},
year = {2022}
}
Comments
v2: Added references; adjusted length. v3: Full-length references