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Scalable graph isomorphism: Combining pairwise color refinement and backtracking via compressed candidate space
Cited 1 time in
Web of Science
Cited 1 time in Scopus
- Authors
- Issue Date
- 2021-04
- Publisher
- IEEE
- Citation
- Proceedings - International Conference on Data Engineering, Vol.2021-April, pp.1368-1379
- 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.
- ISSN
- 1084-4627
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