Local Detour Centrality: A Novel Local Centrality Measure for Weighted Networks

TL;DR

This paper introduces Local Detour Centrality, a new local measure for weighted networks that assesses how vertices facilitate shortest paths, with applications in semantic networks and memory retrieval.

Contribution

It proposes a novel centrality measure that captures local path-shortening effects and demonstrates its distinctiveness and relevance in semantic and memory networks.

Findings

Local Detour Centrality differs from traditional measures like betweenness and degree.

Words with high Local Detour Centrality are linked to greater contextual diversity.

Higher Local Detour Centrality in words correlates with better memory retrieval.

Abstract

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.