Local detour centrality: a novel local centrality measure for weighted networks
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2022-10-01
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| Series: | Applied Network Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s41109-022-00511-w |
| _version_ | 1811257084833955840 |
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| author | Haim Cohen Yinon Nachshon Paz M. Naim Jürgen Jost Emil Saucan Anat Maril |
| author_facet | Haim Cohen Yinon Nachshon Paz M. Naim Jürgen Jost Emil Saucan Anat Maril |
| author_sort | Haim Cohen |
| collection | DOAJ |
| description | 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. Contextually diverse words themselves, however, are not retrieved significantly faster than non-contextually diverse words. These results were obtained for a serial semantic memory task, where the word’s location constitutes a significant mediator in the relationship between the proposed measure and accessibility to knowledge stored in memory. |
| first_indexed | 2024-04-12T17:52:05Z |
| format | Article |
| id | doaj.art-ac6c6ccc307e4783bf44b68beceb218d |
| institution | Directory Open Access Journal |
| issn | 2364-8228 |
| language | English |
| last_indexed | 2024-04-12T17:52:05Z |
| publishDate | 2022-10-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Applied Network Science |
| spelling | doaj.art-ac6c6ccc307e4783bf44b68beceb218d2022-12-22T03:22:29ZengSpringerOpenApplied Network Science2364-82282022-10-017112510.1007/s41109-022-00511-wLocal detour centrality: a novel local centrality measure for weighted networksHaim Cohen0Yinon Nachshon1Paz M. Naim2Jürgen Jost3Emil Saucan4Anat Maril5Department of Cognitive Science, The Hebrew University of JerusalemDepartment of Cognitive Science, The Hebrew University of JerusalemDepartment of Cognitive Science, The Hebrew University of JerusalemMax Planck Institute for Mathematics in the SciencesDepartment of Applied Mathematics, ORT Braude CollegeDepartment of Cognitive Science, The Hebrew University of JerusalemAbstract 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. Contextually diverse words themselves, however, are not retrieved significantly faster than non-contextually diverse words. These results were obtained for a serial semantic memory task, where the word’s location constitutes a significant mediator in the relationship between the proposed measure and accessibility to knowledge stored in memory.https://doi.org/10.1007/s41109-022-00511-wComplex networkCentrality measureSemantic networkSerial taskSemantic retrievalContextual diversity |
| spellingShingle | Haim Cohen Yinon Nachshon Paz M. Naim Jürgen Jost Emil Saucan Anat Maril Local detour centrality: a novel local centrality measure for weighted networks Applied Network Science Complex network Centrality measure Semantic network Serial task Semantic retrieval Contextual diversity |
| title | Local detour centrality: a novel local centrality measure for weighted networks |
| title_full | Local detour centrality: a novel local centrality measure for weighted networks |
| title_fullStr | Local detour centrality: a novel local centrality measure for weighted networks |
| title_full_unstemmed | Local detour centrality: a novel local centrality measure for weighted networks |
| title_short | Local detour centrality: a novel local centrality measure for weighted networks |
| title_sort | local detour centrality a novel local centrality measure for weighted networks |
| topic | Complex network Centrality measure Semantic network Serial task Semantic retrieval Contextual diversity |
| url | https://doi.org/10.1007/s41109-022-00511-w |
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