Scalable graph isomorphism: Combining pairwise color refinement and backtracking via compressed candidate space
SCOPUS
- Title
- Scalable graph isomorphism: Combining pairwise color refinement and backtracking via compressed candidate space
- Authors
- Gu, G.; Nam, Y.; Park, K.; Galil, Z.; Italiano, G.F.; Han, W.-S.
- Date Issued
- 2021-04
- Publisher
- IEEE Computer Society
- Abstract
- 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. ? 2021 IEEE.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/113314
- DOI
- 10.1109/ICDE51399.2021.00122
- ISSN
- 1084-4627
- Article Type
- Article
- Citation
- Proceedings - International Conference on Data Engineering, vol. 2021-April, page. 1368 - 1379, 2021-04
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