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Evolution Model of Spatial Interaction Network in Online Social Networking Services

1
College of System Engineering, National University of Defense Technology, Changsha 410073, China
2
Computer Network Information Center, Chinese Academy of Sciences, 4th South Fourth Road Zhongguancun, Beijing 100190, China
3
University of Chinese Academy of Sciences, 19th Yuquan Road, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(4), 434; https://doi.org/10.3390/e21040434
Received: 22 March 2019 / Revised: 15 April 2019 / Accepted: 23 April 2019 / Published: 24 April 2019
(This article belongs to the Special Issue Computation in Complex Networks)
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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. View Full-Text
Keywords: city interaction network; evolution model; preferential attachment; WeChat; maximum likelihood city interaction network; evolution model; preferential attachment; WeChat; maximum likelihood
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Dong, J.; Chen, B.; Zhang, P.; Ai, C.; Zhang, F.; Guo, D.; Qiu, X. Evolution Model of Spatial Interaction Network in Online Social Networking Services. Entropy 2019, 21, 434.

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