Subgraphs of Interest Social Networks for Diffusion Dynamics Prediction
Finding the building blocks of real-world networks contributes to the understanding of their formation process and related dynamical processes, which is related to prediction and control tasks. We explore different types of social networks, demonstrating high structural variability, and aim to extra...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8074582/ https://www.ncbi.nlm.nih.gov/pubmed/33924216 http://dx.doi.org/10.3390/e23040492 |
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author | Guleva, Valentina Y. Andreeva, Polina O. Vaganov, Danila A. |
author_facet | Guleva, Valentina Y. Andreeva, Polina O. Vaganov, Danila A. |
author_sort | Guleva, Valentina Y. |
collection | PubMed |
description | Finding the building blocks of real-world networks contributes to the understanding of their formation process and related dynamical processes, which is related to prediction and control tasks. We explore different types of social networks, demonstrating high structural variability, and aim to extract and see their minimal building blocks, which are able to reproduce supergraph structural and dynamical properties, so as to be appropriate for diffusion prediction for the whole graph on the base of its small subgraph. For this purpose, we determine topological and functional formal criteria and explore sampling techniques. Using the method that provides the best correspondence to both criteria, we explore the building blocks of interest networks. The best sampling method allows one to extract subgraphs of optimal 30 nodes, which reproduce path lengths, clustering, and degree particularities of an initial graph. The extracted subgraphs are different for the considered interest networks, and provide interesting material for the global dynamics exploration on the mesoscale base. |
format | Online Article Text |
id | pubmed-8074582 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-80745822021-04-27 Subgraphs of Interest Social Networks for Diffusion Dynamics Prediction Guleva, Valentina Y. Andreeva, Polina O. Vaganov, Danila A. Entropy (Basel) Article Finding the building blocks of real-world networks contributes to the understanding of their formation process and related dynamical processes, which is related to prediction and control tasks. We explore different types of social networks, demonstrating high structural variability, and aim to extract and see their minimal building blocks, which are able to reproduce supergraph structural and dynamical properties, so as to be appropriate for diffusion prediction for the whole graph on the base of its small subgraph. For this purpose, we determine topological and functional formal criteria and explore sampling techniques. Using the method that provides the best correspondence to both criteria, we explore the building blocks of interest networks. The best sampling method allows one to extract subgraphs of optimal 30 nodes, which reproduce path lengths, clustering, and degree particularities of an initial graph. The extracted subgraphs are different for the considered interest networks, and provide interesting material for the global dynamics exploration on the mesoscale base. MDPI 2021-04-20 /pmc/articles/PMC8074582/ /pubmed/33924216 http://dx.doi.org/10.3390/e23040492 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Guleva, Valentina Y. Andreeva, Polina O. Vaganov, Danila A. Subgraphs of Interest Social Networks for Diffusion Dynamics Prediction |
title | Subgraphs of Interest Social Networks for Diffusion Dynamics Prediction |
title_full | Subgraphs of Interest Social Networks for Diffusion Dynamics Prediction |
title_fullStr | Subgraphs of Interest Social Networks for Diffusion Dynamics Prediction |
title_full_unstemmed | Subgraphs of Interest Social Networks for Diffusion Dynamics Prediction |
title_short | Subgraphs of Interest Social Networks for Diffusion Dynamics Prediction |
title_sort | subgraphs of interest social networks for diffusion dynamics prediction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8074582/ https://www.ncbi.nlm.nih.gov/pubmed/33924216 http://dx.doi.org/10.3390/e23040492 |
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