Efficient method for estimating the number of communities in a network
While there exist a wide range of effective methods for community detection in networks, most of them require one to know in advance how many communities one is looking for. Here we present a method for estimating the number of communities in a network using a combination of Bayesian inference with...
| Main Authors: | , , , |
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| Format: | Journal article |
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American Physical Society
2017
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| _version_ | 1797099909636161536 |
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| author | Riolo, M Newman, M Reinert, G Cantwell, G |
| author_facet | Riolo, M Newman, M Reinert, G Cantwell, G |
| author_sort | Riolo, M |
| collection | OXFORD |
| description | While there exist a wide range of effective methods for community detection in networks, most of them require one to know in advance how many communities one is looking for. Here we present a method for estimating the number of communities in a network using a combination of Bayesian inference with a novel prior and an efficient Monte Carlo sampling scheme. We test the method extensively on both real and computer-generated networks, showing that it performs accurately and consistently, even in cases where groups are widely varying in size or structure. |
| first_indexed | 2024-03-07T05:30:14Z |
| format | Journal article |
| id | oxford-uuid:e20031d2-adfb-499c-9d0d-f3c55bec6c59 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T05:30:14Z |
| publishDate | 2017 |
| publisher | American Physical Society |
| record_format | dspace |
| spelling | oxford-uuid:e20031d2-adfb-499c-9d0d-f3c55bec6c592022-03-27T09:58:09ZEfficient method for estimating the number of communities in a networkJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:e20031d2-adfb-499c-9d0d-f3c55bec6c59Symplectic Elements at OxfordAmerican Physical Society2017Riolo, MNewman, MReinert, GCantwell, GWhile there exist a wide range of effective methods for community detection in networks, most of them require one to know in advance how many communities one is looking for. Here we present a method for estimating the number of communities in a network using a combination of Bayesian inference with a novel prior and an efficient Monte Carlo sampling scheme. We test the method extensively on both real and computer-generated networks, showing that it performs accurately and consistently, even in cases where groups are widely varying in size or structure. |
| spellingShingle | Riolo, M Newman, M Reinert, G Cantwell, G Efficient method for estimating the number of communities in a network |
| title | Efficient method for estimating the number of communities in a network |
| title_full | Efficient method for estimating the number of communities in a network |
| title_fullStr | Efficient method for estimating the number of communities in a network |
| title_full_unstemmed | Efficient method for estimating the number of communities in a network |
| title_short | Efficient method for estimating the number of communities in a network |
| title_sort | efficient method for estimating the number of communities in a network |
| work_keys_str_mv | AT riolom efficientmethodforestimatingthenumberofcommunitiesinanetwork AT newmanm efficientmethodforestimatingthenumberofcommunitiesinanetwork AT reinertg efficientmethodforestimatingthenumberofcommunitiesinanetwork AT cantwellg efficientmethodforestimatingthenumberofcommunitiesinanetwork |