Local detour centrality: a novel local centrality measure for weighted networks

Abstract Centrality, in some sense, captures the extent to which a vertex controls flow of information network. Here, we propose Local Detour Centrality as novel centrality-based betweenness measure that shortens paths between neighboring vertices compared alternative paths. After presenting our measure, demonstrate empirically it differs from other leading central measures, such betweenness, degree, closeness, and number triangles. Through an empirical case study, provide possible interpretation for word is characterized by contextual diversity within semantic We then examine relationship accessibility knowledge stored memory. To do so, show words occur several different distinct contexts are significantly more effective facilitating retrieval subsequent than lack this diversity. Contextually diverse themselves, however, not retrieved faster non-contextually words. These results were obtained serial memory task, where word’s location constitutes significant mediator proposed