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dc.contributor.author
Hua, Hao
dc.contributor.author
Hovestadt, Ludger
dc.date.accessioned
2021-01-14T09:08:43Z
dc.date.available
2021-01-14T03:48:07Z
dc.date.available
2021-01-14T09:08:43Z
dc.date.issued
2021-01-08
dc.identifier.issn
2045-2322
dc.identifier.other
10.1038/s41598-020-79507-4
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/462351
dc.identifier.doi
10.3929/ethz-b-000462351
dc.description.abstract
The Erdős-Rényi (ER) random graph G(n, p) analytically characterizes the behaviors in complex networks. However, attempts to fit real-world observations need more sophisticated structures (e.g., multilayer networks), rules (e.g., Achlioptas processes), and projections onto geometric, social, or geographic spaces. The p-adic number system offers a natural representation of hierarchical organization of complex networks. The p-adic random graph interprets n as the cardinality of a set of p-adic numbers. Constructing a vast space of hierarchical structures is equivalent for combining number sequences. Although the giant component is vital in dynamic evolution of networks, the structure of multiple big components is also essential. Fitting the sizes of the few largest components to empirical data was rarely demonstrated. The p-adic ultrametric enables the ER model to simulate multiple big components from the observations of genetic interaction networks, social networks, and epidemics. Community structures lead to multimodal distributions of the big component sizes in networks, which have important implications in intervention of spreading processes.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Nature
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
Mathematics and computing
en_US
dc.subject
Physics
en_US
dc.title
p-adic numbers encode complex networks
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2020-12
ethz.journal.title
Scientific Reports
ethz.journal.volume
11
en_US
ethz.journal.issue
1
en_US
ethz.journal.abbreviated
Sci Rep
ethz.pages.start
17
en_US
ethz.size
11 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
London
ethz.publication.status
published
en_US
ethz.date.deposited
2021-01-14T03:48:14Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-01-14T09:08:54Z
ethz.rosetta.lastUpdated
2024-02-02T12:51:44Z
ethz.rosetta.versionExported
true
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