Community detection on directed networks with missing edges
Identifying significant community structures in networks with incomplete data is a challenging task, as the reliability of solutions diminishes with increasing levels of missing information. However, in many empirical contexts, some information about the uncertainty in the network measurements can b...
| Main Authors: | , , |
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| Format: | Internet publication |
| Language: | English |
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2024
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| _version_ | 1824458898749259776 |
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| author | Pedreschi, N Lambiotte, R Bovet, A |
| author_facet | Pedreschi, N Lambiotte, R Bovet, A |
| author_sort | Pedreschi, N |
| collection | OXFORD |
| description | Identifying significant community structures in networks with incomplete data is a challenging task, as the reliability of solutions diminishes with increasing levels of missing information. However, in many empirical contexts, some information about the uncertainty in the network measurements can be estimated. In this work, we extend the recently developed Flow Stability framework, originally designed for detecting communities in time-varying networks, to address the problem of community detection in weighted, directed networks with missing links. Our approach leverages known uncertainty levels in nodes' out-degrees to enhance the robustness of community detection. Through comparisons on synthetic networks and a real-world network of messaging channels on the Telegram platform, we demonstrate that our method delivers more reliable community structures, even when a significant portion of data is missing. |
| first_indexed | 2025-02-19T04:33:13Z |
| format | Internet publication |
| id | oxford-uuid:7981426a-1d66-4963-8275-d8216de0dd7f |
| institution | University of Oxford |
| language | English |
| last_indexed | 2025-02-19T04:33:13Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | oxford-uuid:7981426a-1d66-4963-8275-d8216de0dd7f2025-01-20T09:26:58ZCommunity detection on directed networks with missing edgesInternet publicationhttp://purl.org/coar/resource_type/c_7ad9uuid:7981426a-1d66-4963-8275-d8216de0dd7fEnglishSymplectic Elements2024Pedreschi, NLambiotte, RBovet, AIdentifying significant community structures in networks with incomplete data is a challenging task, as the reliability of solutions diminishes with increasing levels of missing information. However, in many empirical contexts, some information about the uncertainty in the network measurements can be estimated. In this work, we extend the recently developed Flow Stability framework, originally designed for detecting communities in time-varying networks, to address the problem of community detection in weighted, directed networks with missing links. Our approach leverages known uncertainty levels in nodes' out-degrees to enhance the robustness of community detection. Through comparisons on synthetic networks and a real-world network of messaging channels on the Telegram platform, we demonstrate that our method delivers more reliable community structures, even when a significant portion of data is missing. |
| spellingShingle | Pedreschi, N Lambiotte, R Bovet, A Community detection on directed networks with missing edges |
| title | Community detection on directed networks with missing edges |
| title_full | Community detection on directed networks with missing edges |
| title_fullStr | Community detection on directed networks with missing edges |
| title_full_unstemmed | Community detection on directed networks with missing edges |
| title_short | Community detection on directed networks with missing edges |
| title_sort | community detection on directed networks with missing edges |
| work_keys_str_mv | AT pedreschin communitydetectionondirectednetworkswithmissingedges AT lambiotter communitydetectionondirectednetworkswithmissingedges AT boveta communitydetectionondirectednetworkswithmissingedges |