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...
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Autor*in:	Jian Dong [verfasserIn] Bin Chen [verfasserIn] Pengfei Zhang [verfasserIn] Chuan Ai [verfasserIn] Fang Zhang [verfasserIn] Danhuai Guo [verfasserIn] Xiaogang Qiu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	city interaction network evolution model preferential attachment WeChat maximum likelihood

Übergeordnetes Werk:	In: Entropy - MDPI AG, 2003, 21(2019), 4, p 434
Übergeordnetes Werk:	volume:21 ; year:2019 ; number:4, p 434

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/e21040434

Katalog-ID:	DOAJ025968602

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520			\|a 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.
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allfields	10.3390/e21040434 doi (DE-627)DOAJ025968602 (DE-599)DOAJab513aab010c433781639472ee7bb74a DE-627 ger DE-627 rakwb eng QB460-466 QC1-999 Jian Dong verfasserin aut Evolution Model of Spatial Interaction Network in Online Social Networking Services 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. city interaction network evolution model preferential attachment WeChat maximum likelihood Science Q Astrophysics Physics Bin Chen verfasserin aut Pengfei Zhang verfasserin aut Chuan Ai verfasserin aut Fang Zhang verfasserin aut Danhuai Guo verfasserin aut Xiaogang Qiu verfasserin aut In Entropy MDPI AG, 2003 21(2019), 4, p 434 (DE-627)316340359 (DE-600)2014734-X 10994300 nnns volume:21 year:2019 number:4, p 434 https://doi.org/10.3390/e21040434 kostenfrei https://doaj.org/article/ab513aab010c433781639472ee7bb74a kostenfrei https://www.mdpi.com/1099-4300/21/4/434 kostenfrei https://doaj.org/toc/1099-4300 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2019 4, p 434
spelling	10.3390/e21040434 doi (DE-627)DOAJ025968602 (DE-599)DOAJab513aab010c433781639472ee7bb74a DE-627 ger DE-627 rakwb eng QB460-466 QC1-999 Jian Dong verfasserin aut Evolution Model of Spatial Interaction Network in Online Social Networking Services 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. city interaction network evolution model preferential attachment WeChat maximum likelihood Science Q Astrophysics Physics Bin Chen verfasserin aut Pengfei Zhang verfasserin aut Chuan Ai verfasserin aut Fang Zhang verfasserin aut Danhuai Guo verfasserin aut Xiaogang Qiu verfasserin aut In Entropy MDPI AG, 2003 21(2019), 4, p 434 (DE-627)316340359 (DE-600)2014734-X 10994300 nnns volume:21 year:2019 number:4, p 434 https://doi.org/10.3390/e21040434 kostenfrei https://doaj.org/article/ab513aab010c433781639472ee7bb74a kostenfrei https://www.mdpi.com/1099-4300/21/4/434 kostenfrei https://doaj.org/toc/1099-4300 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2019 4, p 434
allfields_unstemmed	10.3390/e21040434 doi (DE-627)DOAJ025968602 (DE-599)DOAJab513aab010c433781639472ee7bb74a DE-627 ger DE-627 rakwb eng QB460-466 QC1-999 Jian Dong verfasserin aut Evolution Model of Spatial Interaction Network in Online Social Networking Services 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. city interaction network evolution model preferential attachment WeChat maximum likelihood Science Q Astrophysics Physics Bin Chen verfasserin aut Pengfei Zhang verfasserin aut Chuan Ai verfasserin aut Fang Zhang verfasserin aut Danhuai Guo verfasserin aut Xiaogang Qiu verfasserin aut In Entropy MDPI AG, 2003 21(2019), 4, p 434 (DE-627)316340359 (DE-600)2014734-X 10994300 nnns volume:21 year:2019 number:4, p 434 https://doi.org/10.3390/e21040434 kostenfrei https://doaj.org/article/ab513aab010c433781639472ee7bb74a kostenfrei https://www.mdpi.com/1099-4300/21/4/434 kostenfrei https://doaj.org/toc/1099-4300 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2019 4, p 434
allfieldsGer	10.3390/e21040434 doi (DE-627)DOAJ025968602 (DE-599)DOAJab513aab010c433781639472ee7bb74a DE-627 ger DE-627 rakwb eng QB460-466 QC1-999 Jian Dong verfasserin aut Evolution Model of Spatial Interaction Network in Online Social Networking Services 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. city interaction network evolution model preferential attachment WeChat maximum likelihood Science Q Astrophysics Physics Bin Chen verfasserin aut Pengfei Zhang verfasserin aut Chuan Ai verfasserin aut Fang Zhang verfasserin aut Danhuai Guo verfasserin aut Xiaogang Qiu verfasserin aut In Entropy MDPI AG, 2003 21(2019), 4, p 434 (DE-627)316340359 (DE-600)2014734-X 10994300 nnns volume:21 year:2019 number:4, p 434 https://doi.org/10.3390/e21040434 kostenfrei https://doaj.org/article/ab513aab010c433781639472ee7bb74a kostenfrei https://www.mdpi.com/1099-4300/21/4/434 kostenfrei https://doaj.org/toc/1099-4300 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2019 4, p 434
allfieldsSound	10.3390/e21040434 doi (DE-627)DOAJ025968602 (DE-599)DOAJab513aab010c433781639472ee7bb74a DE-627 ger DE-627 rakwb eng QB460-466 QC1-999 Jian Dong verfasserin aut Evolution Model of Spatial Interaction Network in Online Social Networking Services 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. city interaction network evolution model preferential attachment WeChat maximum likelihood Science Q Astrophysics Physics Bin Chen verfasserin aut Pengfei Zhang verfasserin aut Chuan Ai verfasserin aut Fang Zhang verfasserin aut Danhuai Guo verfasserin aut Xiaogang Qiu verfasserin aut In Entropy MDPI AG, 2003 21(2019), 4, p 434 (DE-627)316340359 (DE-600)2014734-X 10994300 nnns volume:21 year:2019 number:4, p 434 https://doi.org/10.3390/e21040434 kostenfrei https://doaj.org/article/ab513aab010c433781639472ee7bb74a kostenfrei https://www.mdpi.com/1099-4300/21/4/434 kostenfrei https://doaj.org/toc/1099-4300 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 21 2019 4, p 434
language	English
source	In Entropy 21(2019), 4, p 434 volume:21 year:2019 number:4, p 434
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topic_facet	city interaction network evolution model preferential attachment WeChat maximum likelihood Science Q Astrophysics Physics
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authorswithroles_txt_mv	Jian Dong @@aut@@ Bin Chen @@aut@@ Pengfei Zhang @@aut@@ Chuan Ai @@aut@@ Fang Zhang @@aut@@ Danhuai Guo @@aut@@ Xiaogang Qiu @@aut@@
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callnumber-first	Q - Science
author	Jian Dong
spellingShingle	Jian Dong misc QB460-466 misc QC1-999 misc city interaction network misc evolution model misc preferential attachment misc WeChat misc maximum likelihood misc Science misc Q misc Astrophysics misc Physics Evolution Model of Spatial Interaction Network in Online Social Networking Services
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topic_title	QB460-466 QC1-999 Evolution Model of Spatial Interaction Network in Online Social Networking Services city interaction network evolution model preferential attachment WeChat maximum likelihood
topic	misc QB460-466 misc QC1-999 misc city interaction network misc evolution model misc preferential attachment misc WeChat misc maximum likelihood misc Science misc Q misc Astrophysics misc Physics
topic_unstemmed	misc QB460-466 misc QC1-999 misc city interaction network misc evolution model misc preferential attachment misc WeChat misc maximum likelihood misc Science misc Q misc Astrophysics misc Physics
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title	Evolution Model of Spatial Interaction Network in Online Social Networking Services
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title_sort	evolution model of spatial interaction network in online social networking services
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title_auth	Evolution Model of Spatial Interaction Network in Online Social Networking Services
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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container_issue	4, p 434
title_short	Evolution Model of Spatial Interaction Network in Online Social Networking Services
url	https://doi.org/10.3390/e21040434 https://doaj.org/article/ab513aab010c433781639472ee7bb74a https://www.mdpi.com/1099-4300/21/4/434 https://doaj.org/toc/1099-4300
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author2	Bin Chen Pengfei Zhang Chuan Ai Fang Zhang Danhuai Guo Xiaogang Qiu
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up_date	2024-07-03T18:14:08.338Z
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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. 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