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  Invasion and effective size of graph-structured populations

Giaimo, S., Arranz, J., & Traulsen, A. (2018). Invasion and effective size of graph-structured populations. PLoS Computational Biology, 14(11): e1006559. doi:10.1371/journal.pcbi.1006559.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0002-9870-4 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-9871-3
Genre: Journal Article

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 Creators:
Giaimo, Stefano1, Author              
Arranz, Jordi1, Author              
Traulsen, Arne1, Author              
Affiliations:
1Department Evolutionary Theory, Max Planck Institute for Evolutionary Biology, Max Planck Society, ou_1445641              

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Free keywords: graphs; eigenvectors; evolutionary theory; natural selection; deletion mutation; directed graphs; graph theory; evolutionary genetics
 Abstract: Author summary Evolving populations, companies, social circles are networks in which genes, resources and ideas circulate. Imagine a useful novelty is introduced in the network: a favorable mutation, a new product concept or a smart idea. Will this novelty be retained and propagated through the network or rather lost? Using tools originally devised for demographic research, we model the dynamics of the very initial spread of a useful novelty in a network. The network structure has a strong impact on these dynamics by affecting the effective network size through random effects. This effective network size, which correlates with the probability that a novelty spreads and is different from the actual size (i.e. number of nodes), varies with network structure. The effective size can even become independent of the actual network size and thus remain very small even for huge networks.

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Language(s): eng - English
 Dates: 2018-02-072018-10-092018-11-122018-11
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1371/journal.pcbi.1006559
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Title: PLoS Computational Biology
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 14 (11) Sequence Number: e1006559 Start / End Page: - Identifier: ISSN: 1553-734X
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000017180_1