Beyond clustering: mean-field dynamics on networks
with arbitrary subgraph composition
- Submitting institution
-
University of Sussex
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 201607_53679
- Type
- D - Journal article
- DOI
-
10.1007/s00285-015-0884-1
- Title of journal
- Journal of Mathematical Biology
- Article number
- -
- First page
- 255
- Volume
- 72
- Issue
- 1
- ISSN
- 0303-6812
- Open access status
- Out of scope for open access requirements
- Month of publication
- April
- Year of publication
- 2015
- URL
-
https://doi.org/10.1007/s00285-015-0884-1
- 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
-
2
- Research group(s)
-
-
- Citation count
- 5
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- "This paper develops a PGF-based rigorous mathematical framework for extending the seminal work of Karrer and Newman to non-fully connected subgraphs. This is a significant extension because motifs (subgraphs that occur more than expected at random) in real-world networks are rarely fully connected. It therefore contributes to the on-going scientific discussion regarding higher-order structure and the need to go beyond clustering (see [1] for a recent citing article by a leading network scientist). It also impacts our understanding of epidemic spreading (see [2]). The code to implement the proposed methods is published [3].
[1] https://doi.org/10.1016/j.csfx.2019.100004
[2] https://doi.org/10.1038/s41598-019-43050-8
[3] https://github.com/martinritchie/PGF-ODEs"
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -