Social Network Analysis: Self-Organizing Map and WINGS by Multiple-Criteria Decision Making

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Author(s): Yuh-Wen Chen (Da-Yeh University, Taiwan)
Copyright: 2021
Pages: 30
Source title: Handbook of Research on Autopoiesis and Self-Sustaining Processes for Organizational Success
Source Author(s)/Editor(s): Małgorzata Pańkowska (University of Economics in Katowice, Poland)
DOI: 10.4018/978-1-7998-6713-5.ch008

Keywords: Business and Organizational Research / Business Science Reference / Knowledge Management and Library Science / Organizational Development

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Abstract

Social network analysis (SNA) is an attractive problem for a long time when social communities were popular since 2010. Scholars like to explore the meaning behind the numerous interactions generated at these social media sites. The primary and essential issue of SNA is to monitor, estimate, and engage the potential influencers who are most relevant and active to network. If we can analyze the social network this way, business enterprises could use minimal efforts to sustain the activity of influential users, improve sales, and enhance their reputations. In this chapter, a research framework based on multiple-criteria decision making (MCDM) is proposed. The authors will show how scholars could use dynamic self-organizing map (SOM) based on multiple-objective evolving algorithm (MOEA) and static weighted influence non-linear gauge system (WINGS) to analyze a social network. Finally, comparisons are made between the innovative approaches and the methods in tradition.

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Social Network Analysis: Self-Organizing Map and WINGS by Multiple-Criteria Decision Making

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