Paper Info
Title
Scalable Graph Isomorphism: Combining Pairwise Color Refinement And Backtracking Via Compressed Candidate Space
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.
Year
DOI
Venue
2021
10.1109/ICDE51399.2021.00122
2021 IEEE 37TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2021)
DocType
ISSN
Citations 
Conference
1084-4627
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Geonmo Gu1102.15
Yehyun Nam200.34
Kunsoo Park31396171.00
Zvi Galil436341426.98
Giuseppe F. Italiano500.34
Wook-Shin Han680557.85