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Scalable graph isomorphism: Combining pairwise color refinement and backtracking via compressed candidate space
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Gu, Geonmo | - |
dc.contributor.author | Nam, Yehyun | - |
dc.contributor.author | Park, Kun Soo | - |
dc.contributor.author | Galil, Zvi | - |
dc.contributor.author | Italiano, Giuseppe F. | - |
dc.contributor.author | Han, Wook-Shin | - |
dc.date.accessioned | 2022-06-24T00:26:09Z | - |
dc.date.available | 2022-06-24T00:26:09Z | - |
dc.date.created | 2022-05-09 | - |
dc.date.issued | 2021-04 | - |
dc.identifier.citation | Proceedings - International Conference on Data Engineering, Vol.2021-April, pp.1368-1379 | - |
dc.identifier.issn | 1084-4627 | - |
dc.identifier.uri | https://hdl.handle.net/10371/183752 | - |
dc.description.abstract | © 2021 IEEE.Graph isomorphism is a core problem in graph analysis of various application domains. Given two graphs, the graph isomorphism problem is to determine whether there exists an isomorphism between them. As real-world graphs are getting bigger and bigger, applications demand practically fast algorithms that can run on large-scale graphs. However, existing approaches such as graph canonization and subgraph isomorphism show limited performances on large-scale graphs either in time or space. In this paper, we propose a new approach to graph isomorphism, which is the framework of pairwise color refinement and efficient backtracking. The main features of our approach are: (1) pairwise color refinement and binary cell mapping (2) compressed CS (candidate space), and (3) partial failing set, which together lead to a much faster and scalable algorithm for graph isomorphism. Extensive experiments with real-world datasets show that our approach outperforms state-of-the-art algorithms by up to orders of magnitude in terms of running time. | - |
dc.language | 영어 | - |
dc.publisher | IEEE | - |
dc.title | Scalable graph isomorphism: Combining pairwise color refinement and backtracking via compressed candidate space | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/ICDE51399.2021.00122 | - |
dc.citation.journaltitle | Proceedings - International Conference on Data Engineering | - |
dc.identifier.wosid | 000687830800114 | - |
dc.identifier.scopusid | 2-s2.0-85112867935 | - |
dc.citation.endpage | 1379 | - |
dc.citation.startpage | 1368 | - |
dc.citation.volume | 2021-April | - |
dc.description.isOpenAccess | N | - |
dc.contributor.affiliatedAuthor | Park, Kun Soo | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
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