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Modeling and Prediction for Processes on Network Graphs

Throughout this book so far, we have seen numerous examples of network graphs that provide representations—useful for various purposes—of the interaction among elements in a system under study. Often, however, it is some quantity (or attribute) associated with each of the elements that ultimately is...

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Detalles Bibliográficos
Autores principales: Kolaczyk, Eric D., Csárdi, Gábor
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7264543/
http://dx.doi.org/10.1007/978-3-030-44129-6_8
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author Kolaczyk, Eric D.
Csárdi, Gábor
author_facet Kolaczyk, Eric D.
Csárdi, Gábor
author_sort Kolaczyk, Eric D.
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description Throughout this book so far, we have seen numerous examples of network graphs that provide representations—useful for various purposes—of the interaction among elements in a system under study. Often, however, it is some quantity (or attribute) associated with each of the elements that ultimately is of most interest. In such settings it frequently is not unreasonable to expect that this quantity be influenced in an important manner by the interactions among the elements. For example, the behaviors and beliefs of people can be strongly influenced by their social interactions; proteins that are more similar to each other, with respect to their DNA sequence information, often are responsible for the same or related functional roles in a cell; computers more easily accessible to a computer infected with a virus may in turn themselves become more quickly infected; and the relative concentration of species in an environment (e.g., animal species in a forest or chemical species in a vat) can vary over time as a result of the nature of the relationships among species.
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spelling pubmed-72645432020-06-02 Modeling and Prediction for Processes on Network Graphs Kolaczyk, Eric D. Csárdi, Gábor Statistical Analysis of Network Data with R Article Throughout this book so far, we have seen numerous examples of network graphs that provide representations—useful for various purposes—of the interaction among elements in a system under study. Often, however, it is some quantity (or attribute) associated with each of the elements that ultimately is of most interest. In such settings it frequently is not unreasonable to expect that this quantity be influenced in an important manner by the interactions among the elements. For example, the behaviors and beliefs of people can be strongly influenced by their social interactions; proteins that are more similar to each other, with respect to their DNA sequence information, often are responsible for the same or related functional roles in a cell; computers more easily accessible to a computer infected with a virus may in turn themselves become more quickly infected; and the relative concentration of species in an environment (e.g., animal species in a forest or chemical species in a vat) can vary over time as a result of the nature of the relationships among species. 2020-06-03 /pmc/articles/PMC7264543/ http://dx.doi.org/10.1007/978-3-030-44129-6_8 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Kolaczyk, Eric D.
Csárdi, Gábor
Modeling and Prediction for Processes on Network Graphs
title Modeling and Prediction for Processes on Network Graphs
title_full Modeling and Prediction for Processes on Network Graphs
title_fullStr Modeling and Prediction for Processes on Network Graphs
title_full_unstemmed Modeling and Prediction for Processes on Network Graphs
title_short Modeling and Prediction for Processes on Network Graphs
title_sort modeling and prediction for processes on network graphs
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7264543/
http://dx.doi.org/10.1007/978-3-030-44129-6_8
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