An overview of social network analysis

Oliveira, Márcia; Gama, João

doi:10.1002/widm.1048

Cited by 107 publications

(70 citation statements)

References 61 publications

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“…The node represents the individual or institution in the network, while the tie represents the content or way of communication [32,33]. Social network analysis (SNA) is the methodical analysis of social networks.…”

Section: Methodsmentioning

confidence: 99%

Research collaboration in health management research communities

Zhang

Qing-hua

et al. 2013

BMC Med Inform Decis Mak

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BackgroundThis study uses scientometrics methodology to reveal the status quo and emerging issues of collaboration in health management.MethodsWe searched all the articles with the keyword “health management” in the period 1999–2011 in Web of Knowledge, then 3067 articles were found. Methods such as Social network analysis (SNA), co-authorship, co-word analysis were used in this study.ResultsAnalysis of the past 13 years of research in the field of health management indicates that, whether the production of scientific research, or authors, institutions and scientific research collaboration at the national level, collaboration behavior has been growing steadily across all collaboration types. However, the international scientific research cooperation about health management study between countries needs to be further encouraged. 17 researchers can be seen as the academic leaders in this field. 37 research institutions play a vital role in the information dissemination and resources control in health management. The component analysis found that 22 research groups can be regarded as the backbone in this field. The 8 institution groups consisting of 33 institutions form the core of this field. USA, UK and Australia lie in the center by cohesive subgroup analysis; Based on keywords analysis, 44 keywords with high frequency such as care, disease, system and model were involved in the health management field.ConclusionsThis study demonstrates that although it is growing steadily, collaboration behavior about health management study needs to be enhanced, especially between different institutions or countries/regions, which would promote the progress and internationalization of health management. Besides, researchers should pay attention to the cooperation of representative scholars and institutions, as well as the hot areas of research, because their experience would help us promote the research development of our nation.

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Section: Methodsmentioning

confidence: 99%

Research collaboration in health management research communities

Zhang

Qing-hua

et al. 2013

BMC Med Inform Decis Mak

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“…The various cities in a region are considered as points in the network, and the economic spatial interaction between cities is considered to be the tie. These points and lines comprise the regional economic spatial association network [45,46].…”

Section: Social Network Analysis Methodsmentioning

confidence: 99%

A Framework for Spatiotemporal Analysis of Regional Economic Agglomeration Patterns

et al. 2018

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Understanding regional economic agglomeration patterns is critical for sustainable economic development, urban planning and proper utilization of regional resources. Taking Guangdong Province of China as the study area, this paper introduces a comprehensive research framework for analyzing regional economic agglomeration patterns and understanding their spatiotemporal characteristics. First, convergence and autocorrelation methods are applied to understand the economic spatial patterns. Then, the intercity spatial interaction model (ISIM) is proposed to measure the strength of interplay among cities, and social network analysis (SNA) based on the ISIM is utilized, which is designed to reveal the network characteristics of economic agglomerations. Finally, we perform a spatial panel data analysis to comprehensively interpret the influences of regional economic agglomerations. The results indicate that from 2001 to 2016, the economy in Guangdong showed a double-core/peripheral pattern of convergence, with strengthened intercity interactions. The strength and external spillover effects of Guangzhou and Shenzhen enhanced, while Foshan and Dongguan had relatively strong absorptive abilities. Moreover, expanding regional communication and cooperation is key to enhancing vigorous economic agglomerations and regional network ties in Guangdong by spatial panel data analysis. Our results show that this is a suitable method of reflecting regional economic agglomeration process and its spatiotemporal pattern.

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“…Machine learning focuses on estimation or prediction by considering an optimal model, whereas the latter concentrates on understanding the relationships between data. Recently, a few related studies applying a machine learning-based method have been reported in various research fields, such as environmental science, geomatics, and social science [39][40][41][42].…”

Section: Decision Tree Using Machine Learningmentioning

confidence: 99%

A Machine Learning Approach to the Residential Relocation Distance of Households in the Seoul Metropolitan Region

Kim

2018

Sustainability

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Abstract:This study aimed to evaluate the applicability of a machine learning approach to the description of residential mobility patterns of households in the Seoul metropolitan region (SMR). The spatial range and temporal scope of the empirical study were set to 2015 to review the most recent residential mobility patterns in the SMR. The analysis data used in this study included the Internal Migration Statistics microdata provided by the Microdata Integrated Service of Statistics Korea. We analysed the residential relocation distance of households in the SMR using machine learning techniques, such as ordinary least squares regression and decision tree regression. The results of this study showed that a decision tree model can be more advantageous than ordinary least squares regression in terms of explanatory power and estimation of moving distance. A large number of residential movements are mainly related to the accessibility to employment markets and some household characteristics. The shortest movements occur when households with two or more members move into densely populated districts. In contrast, job-based residential movements are relatively farther. Furthermore, we derived knowledge on residential relocation distance, which can provide significant information for the urban management of metropolitan residential districts and the construction of reasonable housing policies.

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An overview of social network analysis

Cited by 107 publications

References 61 publications

Research collaboration in health management research communities

Research collaboration in health management research communities

A Framework for Spatiotemporal Analysis of Regional Economic Agglomeration Patterns

A Machine Learning Approach to the Residential Relocation Distance of Households in the Seoul Metropolitan Region

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