scholarly journals Flowbca: A Flow-Based Cluster Algorithm in Stata

2018 ◽  
Vol 18 (3) ◽  
pp. 564-584
Author(s):  
Jordy Meekes ◽  
Wolter H. J. Hassink
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Economic Geography ◽  
Cluster Algorithm ◽  
Relational Data ◽  
Input Output ◽  
Output Analysis ◽  
Input Output Analysis ◽  
Research Fields

In this article, we introduce the Stata implementation of a flow-based cluster algorithm, flowbca, written in Mata. The main purpose of flowbca is to identify clusters based on relational data of flows. We illustrate the command by providing multiple examples of applications from the research fields of economic geography, industrial input–output analysis, and social network analysis.

scholarly journals Social network analysis and mining: challenges and applications

2021 ◽  
Author(s):  
Bruna P. Fonseca ◽  
Priscila C. Albuquerque ◽  
Fabio Zicker ◽  
Carlos M. Morel
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Practical Knowledge ◽  
Relevant Information ◽  
Critical Perspective ◽  
Research Fields ◽  
New Challenges ◽  
Advanced Computing ◽  
Computing Infrastructure

Social network analysis and mining (SNAM) is a powerful tool to dis- close relevant information hidden in large volumes of raw data. Its application to several research fields, powered by automation and advanced computing infrastructure, expanded its use and brought along new challenges. In this paper, we provide a critical perspective on SNAM’s major challenges, by discussing a few examples. We also address some promising applications that can potentially translate SNAM results into practical knowledge.

scholarly journals How the Global Trade Linkages are Evolving? -Insight from Social Network Analysis Based on Global Multi-Regional Input-Output

2021 ◽  
pp. 9-24
Author(s):  
Kaiyao Wu ◽  
Chi Zhou ◽  
Dechun Yan
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Network Structure ◽  
Developed Countries ◽  
Global Trade ◽  
Input Output ◽  
Trade Network ◽  
Global Pattern ◽  
The Developed Countries

With the rise and fall of the trade shares of different countries in the world, does the trade network structure change at the same time? Do the dynamics of countries’ positions differ in the evolution of trade network structure? Based on the latest world input-output database (WIOD), this paper illustrates the accounting models of international trade, and describes the dynamics of the global trade network structure and the countries’ positions. Research shows that China and the emerging countries developed faster than the developed countries during 2000-2014, and play important roles, not only in the trade shares, but also in global trade networks. The innovation of this study is that we present a systemic and explicit portrait of the global pattern of trade linkages between countries based on a set of social network analysis methods, and we find that the dynamic of linkage network is more violative than that of linkage flow.

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