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Ranking themes on co-word networks: Exploring the relationships among different metrics

机译：在共词网络上对主题进行排名：探索不同指标之间的关系

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摘要

As network analysis methods prevail, more metrics are applied to co-word networks to reveal hot topics in a field. However, few studies have examined the relationships among these metrics. To bridge this gap, this study explores the relationships among different ranking metrics, including one frequency-based and six network-based metrics, in order to understand the impact of network structural features on ranking themes on co-word networks. We collected bibliographic data from three disciplines from Web of Science (WoS), and generated 40 simulation networks following the preferential attachment assumption. Correlation analysis on the empirical and simulated networks shows strong relationships among the metrics. Their relationships are consistent across disciplines. The metrics can be categorized into three groups according to the strength of their correlations, where Degree Centrality, H-index, and Coreness are in one group, Betweenness Centrality, Clustering Coefficient, and frequency in another, and Weighted PageRank by itself. Regression analysis on the simulation networks reveals that network topology properties, such as connectivity, sparsity, and aggregation, influence the relationships among selected metrics. In addition, when comparing the top keywords ranked by the metrics in the three disciplines, we found the metrics exhibit different discriminative capacity. Coreness and H-index may be better suited for categorizing keywords rather than ranking keywords. Findings from this study contribute to a better understanding of the relationships among different metrics and provide guidance for using them effectively in different contexts.
机译：随着网络分析方法的流行，更多的度量标准被应用于共词网络以揭示领域中的热门话题。但是，很少有研究检查这些指标之间的关系。为了弥合这种差距，本研究探索了不同排名指标之间的关系，包括一个基于频率的指标和六个基于网络的指标，以便了解网络结构特征对共词网络上的排名主题的影响。我们从Web of Science（WoS）的三个学科收集了书目数据，并按照优先依附假设生成了40个模拟网络。对经验网络和模拟网络的相关分析表明，指标之间存在很强的关系。他们之间的关系在各个学科之间是一致的。可以根据度量的相关强度将其分为三组，其中度中心度，H指数和核心度在一个组中，中间度，聚类系数和频率在另一组中，加权PageRank本身。对模拟网络的回归分析表明，网络拓扑属性（如连接性，稀疏性和聚合性）会影响所选指标之间的关系。此外，在比较三个学科中按指标排名的头等关键字时，我们发现这些指标展现出不同的判别能力。核心度和H指数可能更适合于对关键字进行分类，而不是对关键字进行排名。这项研究的发现有助于更好地理解不同指标之间的关系，并为在不同背景下有效使用它们提供指导。

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