A family of algorithms for computing consensus about node state from network data.

Biological and social networks are composed of heterogeneous nodes that contribute differentially to network structure and function. A number of algorithms have been developed to measure this variation. These algorithms have proven useful for applications that require assigning scores to individual...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Eleanor R Brush, David C Krakauer, Jessica C Flack
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/76e9ff37d78d4200ad469b27fae6d5bd
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:76e9ff37d78d4200ad469b27fae6d5bd
record_format	dspace
spelling	oai:doaj.org-article:76e9ff37d78d4200ad469b27fae6d5bd2021-11-18T05:52:00ZA family of algorithms for computing consensus about node state from network data.1553-734X1553-735810.1371/journal.pcbi.1003109https://doaj.org/article/76e9ff37d78d4200ad469b27fae6d5bd2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23874167/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Biological and social networks are composed of heterogeneous nodes that contribute differentially to network structure and function. A number of algorithms have been developed to measure this variation. These algorithms have proven useful for applications that require assigning scores to individual nodes-from ranking websites to determining critical species in ecosystems-yet the mechanistic basis for why they produce good rankings remains poorly understood. We show that a unifying property of these algorithms is that they quantify consensus in the network about a node's state or capacity to perform a function. The algorithms capture consensus by either taking into account the number of a target node's direct connections, and, when the edges are weighted, the uniformity of its weighted in-degree distribution (breadth), or by measuring net flow into a target node (depth). Using data from communication, social, and biological networks we find that that how an algorithm measures consensus-through breadth or depth- impacts its ability to correctly score nodes. We also observe variation in sensitivity to source biases in interaction/adjacency matrices: errors arising from systematic error at the node level or direct manipulation of network connectivity by nodes. Our results indicate that the breadth algorithms, which are derived from information theory, correctly score nodes (assessed using independent data) and are robust to errors. However, in cases where nodes "form opinions" about other nodes using indirect information, like reputation, depth algorithms, like Eigenvector Centrality, are required. One caveat is that Eigenvector Centrality is not robust to error unless the network is transitive or assortative. In these cases the network structure allows the depth algorithms to effectively capture breadth as well as depth. Finally, we discuss the algorithms' cognitive and computational demands. This is an important consideration in systems in which individuals use the collective opinions of others to make decisions.Eleanor R BrushDavid C KrakauerJessica C FlackPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 9, Iss 7, p e1003109 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Eleanor R Brush David C Krakauer Jessica C Flack A family of algorithms for computing consensus about node state from network data.
description	Biological and social networks are composed of heterogeneous nodes that contribute differentially to network structure and function. A number of algorithms have been developed to measure this variation. These algorithms have proven useful for applications that require assigning scores to individual nodes-from ranking websites to determining critical species in ecosystems-yet the mechanistic basis for why they produce good rankings remains poorly understood. We show that a unifying property of these algorithms is that they quantify consensus in the network about a node's state or capacity to perform a function. The algorithms capture consensus by either taking into account the number of a target node's direct connections, and, when the edges are weighted, the uniformity of its weighted in-degree distribution (breadth), or by measuring net flow into a target node (depth). Using data from communication, social, and biological networks we find that that how an algorithm measures consensus-through breadth or depth- impacts its ability to correctly score nodes. We also observe variation in sensitivity to source biases in interaction/adjacency matrices: errors arising from systematic error at the node level or direct manipulation of network connectivity by nodes. Our results indicate that the breadth algorithms, which are derived from information theory, correctly score nodes (assessed using independent data) and are robust to errors. However, in cases where nodes "form opinions" about other nodes using indirect information, like reputation, depth algorithms, like Eigenvector Centrality, are required. One caveat is that Eigenvector Centrality is not robust to error unless the network is transitive or assortative. In these cases the network structure allows the depth algorithms to effectively capture breadth as well as depth. Finally, we discuss the algorithms' cognitive and computational demands. This is an important consideration in systems in which individuals use the collective opinions of others to make decisions.
format	article
author	Eleanor R Brush David C Krakauer Jessica C Flack
author_facet	Eleanor R Brush David C Krakauer Jessica C Flack
author_sort	Eleanor R Brush
title	A family of algorithms for computing consensus about node state from network data.
title_short	A family of algorithms for computing consensus about node state from network data.
title_full	A family of algorithms for computing consensus about node state from network data.
title_fullStr	A family of algorithms for computing consensus about node state from network data.
title_full_unstemmed	A family of algorithms for computing consensus about node state from network data.
title_sort	family of algorithms for computing consensus about node state from network data.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/76e9ff37d78d4200ad469b27fae6d5bd
work_keys_str_mv	AT eleanorrbrush afamilyofalgorithmsforcomputingconsensusaboutnodestatefromnetworkdata AT davidckrakauer afamilyofalgorithmsforcomputingconsensusaboutnodestatefromnetworkdata AT jessicacflack afamilyofalgorithmsforcomputingconsensusaboutnodestatefromnetworkdata AT eleanorrbrush familyofalgorithmsforcomputingconsensusaboutnodestatefromnetworkdata AT davidckrakauer familyofalgorithmsforcomputingconsensusaboutnodestatefromnetworkdata AT jessicacflack familyofalgorithmsforcomputingconsensusaboutnodestatefromnetworkdata
_version_	1718424736653901824

A family of algorithms for computing consensus about node state from network data.

Ejemplares similares