Inferring general relations between network characteristics from specific network ensembles.

Different network models have been suggested for the topology underlying complex interactions in natural systems. These models are aimed at replicating specific statistical features encountered in real-world networks. However, it is rarely considered to which degree the results obtained for one part...

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Autores principales:	Stefano Cardanobile, Volker Pernice, Moritz Deger, Stefan Rotter
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b4900ebb863146d0aca242c0810aa0d8
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spelling	oai:doaj.org-article:b4900ebb863146d0aca242c0810aa0d82021-11-18T07:16:14ZInferring general relations between network characteristics from specific network ensembles.1932-620310.1371/journal.pone.0037911https://doaj.org/article/b4900ebb863146d0aca242c0810aa0d82012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22701586/?tool=EBIhttps://doaj.org/toc/1932-6203Different network models have been suggested for the topology underlying complex interactions in natural systems. These models are aimed at replicating specific statistical features encountered in real-world networks. However, it is rarely considered to which degree the results obtained for one particular network class can be extrapolated to real-world networks. We address this issue by comparing different classical and more recently developed network models with respect to their ability to generate networks with large structural variability. In particular, we consider the statistical constraints which the respective construction scheme imposes on the generated networks. After having identified the most variable networks, we address the issue of which constraints are common to all network classes and are thus suitable candidates for being generic statistical laws of complex networks. In fact, we find that generic, not model-related dependencies between different network characteristics do exist. This makes it possible to infer global features from local ones using regression models trained on networks with high generalization power. Our results confirm and extend previous findings regarding the synchronization properties of neural networks. Our method seems especially relevant for large networks, which are difficult to map completely, like the neural networks in the brain. The structure of such large networks cannot be fully sampled with the present technology. Our approach provides a method to estimate global properties of under-sampled networks in good approximation. Finally, we demonstrate on three different data sets (C. elegans neuronal network, R. prowazekii metabolic network, and a network of synonyms extracted from Roget's Thesaurus) that real-world networks have statistical relations compatible with those obtained using regression models.Stefano CardanobileVolker PerniceMoritz DegerStefan RotterPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 6, p e37911 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Stefano Cardanobile Volker Pernice Moritz Deger Stefan Rotter Inferring general relations between network characteristics from specific network ensembles.
description	Different network models have been suggested for the topology underlying complex interactions in natural systems. These models are aimed at replicating specific statistical features encountered in real-world networks. However, it is rarely considered to which degree the results obtained for one particular network class can be extrapolated to real-world networks. We address this issue by comparing different classical and more recently developed network models with respect to their ability to generate networks with large structural variability. In particular, we consider the statistical constraints which the respective construction scheme imposes on the generated networks. After having identified the most variable networks, we address the issue of which constraints are common to all network classes and are thus suitable candidates for being generic statistical laws of complex networks. In fact, we find that generic, not model-related dependencies between different network characteristics do exist. This makes it possible to infer global features from local ones using regression models trained on networks with high generalization power. Our results confirm and extend previous findings regarding the synchronization properties of neural networks. Our method seems especially relevant for large networks, which are difficult to map completely, like the neural networks in the brain. The structure of such large networks cannot be fully sampled with the present technology. Our approach provides a method to estimate global properties of under-sampled networks in good approximation. Finally, we demonstrate on three different data sets (C. elegans neuronal network, R. prowazekii metabolic network, and a network of synonyms extracted from Roget's Thesaurus) that real-world networks have statistical relations compatible with those obtained using regression models.
format	article
author	Stefano Cardanobile Volker Pernice Moritz Deger Stefan Rotter
author_facet	Stefano Cardanobile Volker Pernice Moritz Deger Stefan Rotter
author_sort	Stefano Cardanobile
title	Inferring general relations between network characteristics from specific network ensembles.
title_short	Inferring general relations between network characteristics from specific network ensembles.
title_full	Inferring general relations between network characteristics from specific network ensembles.
title_fullStr	Inferring general relations between network characteristics from specific network ensembles.
title_full_unstemmed	Inferring general relations between network characteristics from specific network ensembles.
title_sort	inferring general relations between network characteristics from specific network ensembles.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/b4900ebb863146d0aca242c0810aa0d8
work_keys_str_mv	AT stefanocardanobile inferringgeneralrelationsbetweennetworkcharacteristicsfromspecificnetworkensembles AT volkerpernice inferringgeneralrelationsbetweennetworkcharacteristicsfromspecificnetworkensembles AT moritzdeger inferringgeneralrelationsbetweennetworkcharacteristicsfromspecificnetworkensembles AT stefanrotter inferringgeneralrelationsbetweennetworkcharacteristicsfromspecificnetworkensembles
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Inferring general relations between network characteristics from specific network ensembles.

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