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- Publisher Website: 10.1038/s42005-022-00816-3
- Scopus: eid_2-s2.0-85125248540
- WOS: WOS:000757372900001
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Article: Representative community divisions of networks
Title | Representative community divisions of networks |
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Authors | |
Issue Date | 2022 |
Citation | Communications Physics, 2022, v. 5, n. 1, article no. 40 How to Cite? |
Abstract | Methods for detecting community structure in networks typically aim to identify a single best partition of network nodes into communities, often by optimizing some objective function, but in real-world applications there may be many competitive partitions with objective scores close to the global optimum and one can obtain a more informative picture of the community structure by examining a representative set of such high-scoring partitions than by looking at just the single optimum. However, such a set can be difficult to interpret since its size can easily run to hundreds or thousands of partitions. In this paper we present a method for analyzing large partition sets by dividing them into groups of similar partitions and then identifying an archetypal partition as a representative of each group. The resulting set of archetypal partitions provides a succinct, interpretable summary of the form and variety of community structure in any network. We demonstrate the method on a range of example networks. |
Persistent Identifier | http://hdl.handle.net/10722/317100 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Kirkley, Alec | - |
dc.contributor.author | Newman, M. E.J. | - |
dc.date.accessioned | 2022-09-19T06:18:47Z | - |
dc.date.available | 2022-09-19T06:18:47Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | Communications Physics, 2022, v. 5, n. 1, article no. 40 | - |
dc.identifier.uri | http://hdl.handle.net/10722/317100 | - |
dc.description.abstract | Methods for detecting community structure in networks typically aim to identify a single best partition of network nodes into communities, often by optimizing some objective function, but in real-world applications there may be many competitive partitions with objective scores close to the global optimum and one can obtain a more informative picture of the community structure by examining a representative set of such high-scoring partitions than by looking at just the single optimum. However, such a set can be difficult to interpret since its size can easily run to hundreds or thousands of partitions. In this paper we present a method for analyzing large partition sets by dividing them into groups of similar partitions and then identifying an archetypal partition as a representative of each group. The resulting set of archetypal partitions provides a succinct, interpretable summary of the form and variety of community structure in any network. We demonstrate the method on a range of example networks. | - |
dc.language | eng | - |
dc.relation.ispartof | Communications Physics | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.title | Representative community divisions of networks | - |
dc.type | Article | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1038/s42005-022-00816-3 | - |
dc.identifier.scopus | eid_2-s2.0-85125248540 | - |
dc.identifier.volume | 5 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | article no. 40 | - |
dc.identifier.epage | article no. 40 | - |
dc.identifier.eissn | 2399-3650 | - |
dc.identifier.isi | WOS:000757372900001 | - |