Social Network Analysis
Social Network Analysis
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DOI：
10.4018/978-1-7998-6713-5.ch008
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Yuh-Wen Chen
中科院分区：
文献类型：
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作者：
Yuh-Wen Chen
自2010年以来，社会网络分析（SNA）一直是一个很有吸引力的问题，当时社会社区很流行。学者们喜欢探索这些社交媒体网站上产生的众多互动背后的意义。SNA的首要和基本问题是监测、估计和吸引与网络最相关和最活跃的潜在影响者。如果我们能够以这种方式分析社交网络，企业可以用最小的努力来维持有影响力的用户的活动，提高销售额，并提高他们的声誉。在本章中，提出了一个基于多准则决策的研究框架。作者将展示学者如何使用基于多目标进化算法（MOEA）的动态自组织映射（SOM）和静态加权影响非线性规范系统（WINGS）来分析社会网络。最后，对创新方法与传统方法进行了比较。
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.