Improving the Walktrap Algorithm Using K-Means Clustering.

IF 5.3 3区 心理学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Multivariate Behavioral Research Pub Date : 2024-03-01 Epub Date: 2024-02-15 DOI:10.1080/00273171.2023.2254767
Michael Brusco, Douglas Steinley, Ashley L Watts
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引用次数: 0

Abstract

The walktrap algorithm is one of the most popular community-detection methods in psychological research. Several simulation studies have shown that it is often effective at determining the correct number of communities and assigning items to their proper community. Nevertheless, it is important to recognize that the walktrap algorithm relies on hierarchical clustering because it was originally developed for networks much larger than those encountered in psychological research. In this paper, we present and demonstrate a computational alternative to the hierarchical algorithm that is conceptually easier to understand. More importantly, we show that better solutions to the sum-of-squares optimization problem that is heuristically tackled by hierarchical clustering in the walktrap algorithm can often be obtained using exact or approximate methods for K-means clustering. Three simulation studies and analyses of empirical networks were completed to assess the impact of better sum-of-squares solutions.

利用 K-Means 聚类改进 Walktrap 算法
走马算法是心理学研究中最常用的群体检测方法之一。多项模拟研究表明,该算法通常能有效确定正确的社群数量,并将项目分配到合适的社群中。然而,我们必须认识到,walktrap 算法依赖于分层聚类,因为它最初是针对比心理学研究中遇到的网络大得多的网络而开发的。在本文中,我们提出并演示了分层算法的计算替代方案,这种方案在概念上更容易理解。更重要的是,我们表明,对于分层聚类在走马灯算法中启发式解决的平方和优化问题,通常可以通过 K-means 聚类的精确或近似方法获得更好的解决方案。我们完成了三项模拟研究和经验网络分析,以评估更好的平方和解决方案的影响。
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来源期刊
Multivariate Behavioral Research
Multivariate Behavioral Research 数学-数学跨学科应用
CiteScore
7.60
自引率
2.60%
发文量
49
审稿时长
>12 weeks
期刊介绍: Multivariate Behavioral Research (MBR) publishes a variety of substantive, methodological, and theoretical articles in all areas of the social and behavioral sciences. Most MBR articles fall into one of two categories. Substantive articles report on applications of sophisticated multivariate research methods to study topics of substantive interest in personality, health, intelligence, industrial/organizational, and other behavioral science areas. Methodological articles present and/or evaluate new developments in multivariate methods, or address methodological issues in current research. We also encourage submission of integrative articles related to pedagogy involving multivariate research methods, and to historical treatments of interest and relevance to multivariate research methods.
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