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Evolution Model of Spatial Interaction Network in Online Social Networking Services
The development of online social networking services provides a rich source of data of social networks including geospatial information. More and more research has shown that geographical space is an important factor in the interactions of users in social networks. In this paper, we construct the sp...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514923/ https://www.ncbi.nlm.nih.gov/pubmed/33267148 http://dx.doi.org/10.3390/e21040434 |
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author | Dong, Jian Chen, Bin Zhang, Pengfei Ai, Chuan Zhang, Fang Guo, Danhuai Qiu, Xiaogang |
author_facet | Dong, Jian Chen, Bin Zhang, Pengfei Ai, Chuan Zhang, Fang Guo, Danhuai Qiu, Xiaogang |
author_sort | Dong, Jian |
collection | PubMed |
description | The development of online social networking services provides a rich source of data of social networks including geospatial information. More and more research has shown that geographical space is an important factor in the interactions of users in social networks. In this paper, we construct the spatial interaction network from the city level, which is called the city interaction network, and study the evolution mechanism of the city interaction network formed in the process of information dissemination in social networks. A network evolution model for interactions among cities is established. The evolution model consists of two core processes: the edge arrival and the preferential attachment of the edge. The edge arrival model arranges the arrival time of each edge; the model of preferential attachment of the edge determines the source node and the target node of each arriving edge. Six preferential attachment models (Random-Random, Random-Degree, Degree-Random, Geographical distance, Degree-Degree, Degree-Degree-Geographical distance) are built, and the maximum likelihood approach is used to do the comparison. We find that the degree of the node and the geographic distance of the edge are the key factors affecting the evolution of the city interaction network. Finally, the evolution experiments using the optimal model DDG are conducted, and the experiment results are compared with the real city interaction network extracted from the information dissemination data of the WeChat web page. The results indicate that the model can not only capture the attributes of the real city interaction network, but also reflect the actual characteristics of the interactions among cities. |
format | Online Article Text |
id | pubmed-7514923 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75149232020-11-09 Evolution Model of Spatial Interaction Network in Online Social Networking Services Dong, Jian Chen, Bin Zhang, Pengfei Ai, Chuan Zhang, Fang Guo, Danhuai Qiu, Xiaogang Entropy (Basel) Article The development of online social networking services provides a rich source of data of social networks including geospatial information. More and more research has shown that geographical space is an important factor in the interactions of users in social networks. In this paper, we construct the spatial interaction network from the city level, which is called the city interaction network, and study the evolution mechanism of the city interaction network formed in the process of information dissemination in social networks. A network evolution model for interactions among cities is established. The evolution model consists of two core processes: the edge arrival and the preferential attachment of the edge. The edge arrival model arranges the arrival time of each edge; the model of preferential attachment of the edge determines the source node and the target node of each arriving edge. Six preferential attachment models (Random-Random, Random-Degree, Degree-Random, Geographical distance, Degree-Degree, Degree-Degree-Geographical distance) are built, and the maximum likelihood approach is used to do the comparison. We find that the degree of the node and the geographic distance of the edge are the key factors affecting the evolution of the city interaction network. Finally, the evolution experiments using the optimal model DDG are conducted, and the experiment results are compared with the real city interaction network extracted from the information dissemination data of the WeChat web page. The results indicate that the model can not only capture the attributes of the real city interaction network, but also reflect the actual characteristics of the interactions among cities. MDPI 2019-04-24 /pmc/articles/PMC7514923/ /pubmed/33267148 http://dx.doi.org/10.3390/e21040434 Text en © 2019 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Dong, Jian Chen, Bin Zhang, Pengfei Ai, Chuan Zhang, Fang Guo, Danhuai Qiu, Xiaogang Evolution Model of Spatial Interaction Network in Online Social Networking Services |
title | Evolution Model of Spatial Interaction Network in Online Social Networking Services |
title_full | Evolution Model of Spatial Interaction Network in Online Social Networking Services |
title_fullStr | Evolution Model of Spatial Interaction Network in Online Social Networking Services |
title_full_unstemmed | Evolution Model of Spatial Interaction Network in Online Social Networking Services |
title_short | Evolution Model of Spatial Interaction Network in Online Social Networking Services |
title_sort | evolution model of spatial interaction network in online social networking services |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7514923/ https://www.ncbi.nlm.nih.gov/pubmed/33267148 http://dx.doi.org/10.3390/e21040434 |
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