Ritchie, Martin, Berthouze, Luc and Kiss, Istvan Z (2016) Beyond clustering: mean-field dynamics on networks with arbitrary subgraph composition. Journal of Mathematical Biology, 72 (1). pp. 255-281. ISSN 0303-6812

Preview

PDF - Published Version Available under License Creative Commons Attribution. Download (579kB) | Preview

Abstract

Clustering is the propensity of nodes that share a common neighbour to be connected. It is ubiquitous in many networks but poses many modelling challenges. Clustering typically manifests itself by a higher than expected frequency of triangles, and this has led to the principle of constructing networks from such building blocks. This approach has been generalised to networks being constructed from a set of more exotic subgraphs. As long as these are fully connected, it is then possible to derive mean-field models that approximate epidemic dynamics well. However, there are virtually no results for non-fully connected subgraphs. In this paper, we provide a general and automated approach to deriving a set of ordinary differential equations, or mean-field model, that describes, to a high degree of accuracy, the expected values of system-level quantities, such as the prevalence of infection. Our approach offers a previously unattainable degree of control over the arrangement of subgraphs and network characteristics such as classical node degree, variance and clustering. The combination of these features makes it possible to generate families of networks with different subgraph compositions while keeping classical network metrics constant.
Using our approach, we show that higher-order structure realised either through the introduction of loops of different sizes or by generating clustered networks based on different subgraphs, leads to significant differences in epidemic dynamics despite controlling for basic network metrics.

Item Type:	Article
Additional Information:	Author's post-print will be available to view 12 months after publication
Schools and Departments:	School of Engineering and Informatics > Informatics School of Mathematical and Physical Sciences > Mathematics
Subjects:	Q Science > QA Mathematics
Depositing User:	Istvan Kiss
Date Deposited:	16 Apr 2015 11:35
Last Modified:	24 Jun 2017 07:42
URI:	http://srodev.sussex.ac.uk/id/eprint/53679

View download statistics for this item

📧 Request an update