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

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

Abstract

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.

Citation

Cui, K., Khudabukhsh, W. R., & Koeppl, H. (2022). Motif-based mean-field approximation of interacting particles on clustered networks. Physical Review E, 105(4), Article L042301. https://doi.org/10.1103/PhysRevE.105.L042301

• Journal Article Type: Article
• Acceptance Date: Apr 7, 2022
• Online Publication Date: Apr 28, 2022
• Publication Date: Apr 1, 2022
• Deposit Date: Apr 29, 2022
• Publicly Available Date: May 6, 2022
• Journal: Physical Review E
• Print ISSN: 2470-0045
• Electronic ISSN: 2470-0053
• Publisher: American Physical Society (APS)
• Peer Reviewed: Peer Reviewed
• Volume: 105
• Issue: 4
• Article Number: L042301
• DOI: https://doi.org/10.1103/PhysRevE.105.L042301
• Public URL: https://nottingham-repository.worktribe.com/output/7839832
• Publisher URL: https://journals.aps.org/pre/abstract/10.1103/PhysRevE.105.L042301
• Additional Information: ©2022 American Physical Society