Higher-order structure and epidemic dynamics in clustered networks
- Submitting institution
-
University of Sussex
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 201607_47582
- Type
- D - Journal article
- DOI
-
10.1016/j.jtbi.2014.01.025
- Title of journal
- Journal of Theoretical Biology
- Article number
- -
- First page
- 21
- Volume
- 348
- Issue
- -
- ISSN
- 0022-5193
- Open access status
- Out of scope for open access requirements
- Month of publication
- January
- Year of publication
- 2014
- URL
-
https://doi.org/10.1016/j.jtbi.2014.01.025
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- 14
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- "This paper developed new thinking in modelling epidemics by providing empirical evidence that the traditional approach of describing networks in terms of degree distribution and clustering overlooked important higher-order differences. It laid the foundation for a rigorous attempt at including more complex subgraphs in the configuration model [1] as well as a method for generating networks parametrised by their subgraph decomposition and satisfying constraints on their degree distribution and global clustering coefficient [2]. The paper was article of the month in April 2014. The code to implement the proposed methods is published [3].
[1] https://doi.org/10.1007/s00285-015-0884-1
[2] https://doi.org/10.1093/comnet/cnw011
[3] https://github.com/martinritchie/Network-generation-algorithms"
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -