Cargando…
A neural network framework for predicting dynamic variations in heterogeneous social networks
Forecasting possible future relationships between people in a network requires a study of the evolution of their links. To capture network dynamics and temporal variations in link strengths between various types of nodes in a network, a dynamic weighted heterogeneous network is to be considered. Lin...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7185585/ https://www.ncbi.nlm.nih.gov/pubmed/32339174 http://dx.doi.org/10.1371/journal.pone.0231842 |
_version_ | 1783526786440626176 |
---|---|
author | Balakrishnan, Mathiarasi T. V., Geetha |
author_facet | Balakrishnan, Mathiarasi T. V., Geetha |
author_sort | Balakrishnan, Mathiarasi |
collection | PubMed |
description | Forecasting possible future relationships between people in a network requires a study of the evolution of their links. To capture network dynamics and temporal variations in link strengths between various types of nodes in a network, a dynamic weighted heterogeneous network is to be considered. Link strength prediction in such networks is still an open problem. Moreover, a study of variations in link strengths with respect to time has not yet been explored. The time granularity at which the weights of various links change remains to be delved into. To tackle these problems, we propose a neural network framework to predict dynamic variations in weighted heterogeneous social networks. Our link strength prediction model predicts future relationships between people, along with a measure of the strength of those relationships. The experimental results highlight the fact that link weights and dynamism greatly impact the performance of link strength prediction. |
format | Online Article Text |
id | pubmed-7185585 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-71855852020-05-06 A neural network framework for predicting dynamic variations in heterogeneous social networks Balakrishnan, Mathiarasi T. V., Geetha PLoS One Research Article Forecasting possible future relationships between people in a network requires a study of the evolution of their links. To capture network dynamics and temporal variations in link strengths between various types of nodes in a network, a dynamic weighted heterogeneous network is to be considered. Link strength prediction in such networks is still an open problem. Moreover, a study of variations in link strengths with respect to time has not yet been explored. The time granularity at which the weights of various links change remains to be delved into. To tackle these problems, we propose a neural network framework to predict dynamic variations in weighted heterogeneous social networks. Our link strength prediction model predicts future relationships between people, along with a measure of the strength of those relationships. The experimental results highlight the fact that link weights and dynamism greatly impact the performance of link strength prediction. Public Library of Science 2020-04-27 /pmc/articles/PMC7185585/ /pubmed/32339174 http://dx.doi.org/10.1371/journal.pone.0231842 Text en © 2020 Balakrishnan, T. V http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Balakrishnan, Mathiarasi T. V., Geetha A neural network framework for predicting dynamic variations in heterogeneous social networks |
title | A neural network framework for predicting dynamic variations in heterogeneous social networks |
title_full | A neural network framework for predicting dynamic variations in heterogeneous social networks |
title_fullStr | A neural network framework for predicting dynamic variations in heterogeneous social networks |
title_full_unstemmed | A neural network framework for predicting dynamic variations in heterogeneous social networks |
title_short | A neural network framework for predicting dynamic variations in heterogeneous social networks |
title_sort | neural network framework for predicting dynamic variations in heterogeneous social networks |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7185585/ https://www.ncbi.nlm.nih.gov/pubmed/32339174 http://dx.doi.org/10.1371/journal.pone.0231842 |
work_keys_str_mv | AT balakrishnanmathiarasi aneuralnetworkframeworkforpredictingdynamicvariationsinheterogeneoussocialnetworks AT tvgeetha aneuralnetworkframeworkforpredictingdynamicvariationsinheterogeneoussocialnetworks AT balakrishnanmathiarasi neuralnetworkframeworkforpredictingdynamicvariationsinheterogeneoussocialnetworks AT tvgeetha neuralnetworkframeworkforpredictingdynamicvariationsinheterogeneoussocialnetworks |