论文标题
当地弯路中心：加权网络的新型当地中心度度量
Local Detour Centrality: A Novel Local Centrality Measure for Weighted Networks
论文作者
论文摘要
从某种意义上说，中心性捕获了顶点控制网络中信息流的程度。在这里，我们提出局部弯路中心性作为一种基于新型中心性的中间度度量，该中心度捕获了与替代路径相比，顶点缩短相邻顶点之间的路径的程度。介绍了我们的度量后，我们从经验上证明了它与其他领先的中心措施（例如中心，程度，亲密关系和三角形数量）有所不同。通过经验案例研究，我们为局部弯路中心性提供了一种可能的解释，作为捕获单词在语义网络中以上下文多样性为特征的程度的度量。然后，我们检查我们的度量与存储在内存中的知识的可访问性之间的关系。为此，我们表明在几个不同和不同的环境中发生的单词在促进后续单词的检索方面比缺乏这种上下文多样性的单词更有效。
Centrality, in some sense, captures the extent to which a vertex controls the flow of information in a network. Here, we propose Local Detour Centrality as a novel centrality-based betweenness measure that captures the extent to which a vertex shortens paths between neighboring vertices as compared to alternative paths. After presenting our measure, we demonstrate empirically that it differs from other leading central measures, such as betweenness, degree, closeness, and the number of triangles. Through an empirical case study, we provide a possible interpretation for Local Detour Centrality as a measure that captures the extent to which a word is characterized by contextual diversity within a semantic network. We then examine the relationship between our measure and the accessibility to knowledge stored in memory. To do so, we show that words that occur in several different and distinct contexts are significantly more effective in facilitating the retrieval of subsequent words than are words that lack this contextual diversity.
