Generation and analysis of networks with a prescribed degree sequence and subgraph family: higher-order structure matters
- Submitting institution
-
University of Sussex
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 201607_60762
- Type
- D - Journal article
- DOI
-
10.1093/comnet/cnw011
- Title of journal
- Journal of Complex Networks
- Article number
- -
- First page
- 1
- Volume
- 5
- Issue
- 1
- ISSN
- 2051-1310
- Open access status
- Out of scope for open access requirements
- Month of publication
- May
- Year of publication
- 2016
- URL
-
https://doi.org/10.1093/comnet/cnw011
- 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
- 10
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- "This paper is the first to enable the generation of networks defined by a subgraph decomposition and satisfying constraints on their degree distribution and global clustering coefficient. It therefore lays the foundation for a rigorous approach to generating meaningful null models for clustered networks. As recognised by a paper co-authored by the influential Jürgen Kurths [1], the ability to generate such random networks has ""naturally motivated the study of well-known dynamical process in these structures, such as in percolation and epidemic spreading, cascade failure and synchronization"". Code for the proposed methods is publicly available [2].
[1] http://doi.org/10.1209/0295-5075/121/68001
[2] https://github.com/martinritchie/Network-generation-algorithms."
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -