Publisher Correction: Finding influential nodes for integration in brain networks using optimal percolation theory
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
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2041-1723
- Extent
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Online-Ressource
- Language
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Englisch
- Notes
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online resource.
- Bibliographic citation
-
Publisher Correction: Finding influential nodes for integration in brain networks using optimal percolation theory ; volume:9 ; number:1 ; day:3 ; month:8 ; year:2018 ; pages:1 ; date:12.2018
Nature Communications ; 9, Heft 1 (3.8.2018), 1, 12.2018
- Classification
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Allgemeines, Wissenschaft
- Creator
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Ferraro, Gino Del
- Contributor
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Moreno, Andrea
Min, Byungjoon
Morone, Flaviano
Pérez-Ramírez, Úrsula
Pérez-Cervera, Laura
Parra, Lucas C.
Holodny, Andrei I.
Canals, Santiago
Makse, Hernán A.
SpringerLink (Online service)
- DOI
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10.1038/s41467-018-05686-4
- URN
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urn:nbn:de:101:1-2018100421502246754266
- Rights
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Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:24 AM CEST
Data provider
This object is provided by:
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Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Ferraro, Gino Del
- Moreno, Andrea
- Min, Byungjoon
- Morone, Flaviano
- Pérez-Ramírez, Úrsula
- Pérez-Cervera, Laura
- Parra, Lucas C.
- Holodny, Andrei I.
- Canals, Santiago
- Makse, Hernán A.
- SpringerLink (Online service)
Other Objects (12)
Finding influential nodes for integration in brain networks using optimal percolation theory
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Finding influential nodes in complex networks based on Kullback–Leibler model within the neighborhood
Exploring influential nodes using global and local information
Influential nodes identification in complex networks: a comprehensive literature review
Identifying influential nodes in complex networks based on Neighbours and edges
Spheres of legislation: polarization and most influential nodes in behavioral context
Identifying influential spreaders by gravity model considering multi-characteristics of nodes
Integrating local and global information to identify influential nodes in complex networks
Identifying influential nodes based on the disassortativity and community structure of complex network
Percolation
Finding influential nodes for integration in brain networks using optimal percolation theory
Publisher Correction: Improved collective influence of finding most influential nodes based on disjoint-set reinsertion
Improved collective influence of finding most influential nodes based on disjoint-set reinsertion
Finding influential nodes in complex networks based on Kullback–Leibler model within the neighborhood
Exploring influential nodes using global and local information
Influential nodes identification in complex networks: a comprehensive literature review
Identifying influential nodes in complex networks based on Neighbours and edges
Spheres of legislation: polarization and most influential nodes in behavioral context
Identifying influential spreaders by gravity model considering multi-characteristics of nodes
Integrating local and global information to identify influential nodes in complex networks
Identifying influential nodes based on the disassortativity and community structure of complex network
Percolation
Finding influential nodes for integration in brain networks using optimal percolation theory
Publisher Correction: Improved collective influence of finding most influential nodes based on disjoint-set reinsertion
Improved collective influence of finding most influential nodes based on disjoint-set reinsertion
Finding influential nodes in complex networks based on Kullback–Leibler model within the neighborhood
Exploring influential nodes using global and local information
Influential nodes identification in complex networks: a comprehensive literature review
Identifying influential nodes in complex networks based on Neighbours and edges
Spheres of legislation: polarization and most influential nodes in behavioral context
Identifying influential spreaders by gravity model considering multi-characteristics of nodes
Integrating local and global information to identify influential nodes in complex networks
Identifying influential nodes based on the disassortativity and community structure of complex network