Parallel Algorithms for Hierarchical Nucleus Decomposition
Nucleus decompositions have been shown to be a useful tool for finding dense subgraphs. The coreness value of a clique represents its density based on the number of other cliques it is adjacent to. One useful output of nucleus decomposition is to generate a hierarchy among dense subgraphs at differe...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Association for Computing Machinery
2024
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| Online Access: | https://hdl.handle.net/1721.1/154067 |
| _version_ | 1824457915257323520 |
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| author | Shi, Jessica Dhulipala, Laxman Shun, Julian |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Shi, Jessica Dhulipala, Laxman Shun, Julian |
| author_sort | Shi, Jessica |
| collection | MIT |
| description | Nucleus decompositions have been shown to be a useful tool for finding dense subgraphs. The coreness value of a clique represents its density based on the number of other cliques it is adjacent to. One useful output of nucleus decomposition is to generate a hierarchy among dense subgraphs at different resolutions. However, existing parallel algorithms for nucleus decomposition do not generate this hierarchy, and only compute the coreness values. This paper presents a scalable parallel algorithm for hierarchy construction, with practical optimizations, such as interleaving the coreness computation with hierarchy construction and using a concurrent union-find data structure in an innovative way to generate the hierarchy. We also introduce a parallel approximation algorithm for nucleus decomposition, which achieves much lower span in theory and better performance in practice. We prove strong theoretical bounds on the work and span (parallel time) of our algorithms.
On a 30-core machine with two-way hyper-threading, our parallel hierarchy construction algorithm achieves up to a 58.84x speedup over the state-of-the-art sequential hierarchy construction algorithm by Sariyuce et al. and up to a 30.96x self-relative parallel speedup. On the same machine, our approximation algorithm achieves a 3.3x speedup over our exact algorithm, while generating coreness estimates with a multiplicative error of 1.33x on average. |
| first_indexed | 2024-09-23T08:59:35Z |
| format | Article |
| id | mit-1721.1/154067 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2025-02-19T04:17:35Z |
| publishDate | 2024 |
| publisher | Association for Computing Machinery |
| record_format | dspace |
| spelling | mit-1721.1/1540672024-12-23T05:55:59Z Parallel Algorithms for Hierarchical Nucleus Decomposition Shi, Jessica Dhulipala, Laxman Shun, Julian Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Nucleus decompositions have been shown to be a useful tool for finding dense subgraphs. The coreness value of a clique represents its density based on the number of other cliques it is adjacent to. One useful output of nucleus decomposition is to generate a hierarchy among dense subgraphs at different resolutions. However, existing parallel algorithms for nucleus decomposition do not generate this hierarchy, and only compute the coreness values. This paper presents a scalable parallel algorithm for hierarchy construction, with practical optimizations, such as interleaving the coreness computation with hierarchy construction and using a concurrent union-find data structure in an innovative way to generate the hierarchy. We also introduce a parallel approximation algorithm for nucleus decomposition, which achieves much lower span in theory and better performance in practice. We prove strong theoretical bounds on the work and span (parallel time) of our algorithms. On a 30-core machine with two-way hyper-threading, our parallel hierarchy construction algorithm achieves up to a 58.84x speedup over the state-of-the-art sequential hierarchy construction algorithm by Sariyuce et al. and up to a 30.96x self-relative parallel speedup. On the same machine, our approximation algorithm achieves a 3.3x speedup over our exact algorithm, while generating coreness estimates with a multiplicative error of 1.33x on average. 2024-04-04T16:26:40Z 2024-04-04T16:26:40Z 2024-03-12 2024-04-01T07:48:58Z Article http://purl.org/eprint/type/JournalArticle 2836-6573 https://hdl.handle.net/1721.1/154067 Jessica Shi, Laxman Dhulipala, and Julian Shun. 2024. Parallel Algorithms for Hierarchical Nucleus Decomposition. Proc. ACM Manag. Data 2, 1 (SIGMOD), Article 32 (February 2024), 27 pages. PUBLISHER_POLICY en 10.1145/3639287 Proceedings of the ACM on Management of Data Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. The author(s) application/pdf Association for Computing Machinery ACM |
| spellingShingle | Shi, Jessica Dhulipala, Laxman Shun, Julian Parallel Algorithms for Hierarchical Nucleus Decomposition |
| title | Parallel Algorithms for Hierarchical Nucleus Decomposition |
| title_full | Parallel Algorithms for Hierarchical Nucleus Decomposition |
| title_fullStr | Parallel Algorithms for Hierarchical Nucleus Decomposition |
| title_full_unstemmed | Parallel Algorithms for Hierarchical Nucleus Decomposition |
| title_short | Parallel Algorithms for Hierarchical Nucleus Decomposition |
| title_sort | parallel algorithms for hierarchical nucleus decomposition |
| url | https://hdl.handle.net/1721.1/154067 |
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