Title
Efficient Graph Isomorphism Query Processing using Degree Sequences and Color-Label Distributions
Abstract
Given a set of data graphs and a query graph, graph isomorphism query processing is the problem of finding all the data graphs that are isomorphic to the query graph. Graph isomorphism query processing is a core problem in graph analysis of various application domains. In existing approaches, index construction or query processing takes much time as the graph sizes increase. In this paper, we propose an efficient algorithm for graph isomorphism query processing. We introduce the color-label distribution which represents the canonical coloring of a vertex-labeled graph. Based on degree sequences and color-label distributions, we introduce a two-level index, which helps us efficiently solve graph isomorphism query processing. Experimental results on real datasets show that the proposed algorithm is orders of magnitude faster than the state-of-the-art algorithms in terms of index construction time, and it runs faster than existing algorithms in terms of query processing time as the graph sizes increase.
Year
DOI
Venue
2022
10.1109/ICDE53745.2022.00070
2022 IEEE 38th International Conference on Data Engineering (ICDE)
Keywords
DocType
ISSN
graph isomorphism query processing,degree sequence,color-label distribution,canonical coloring,graph canonization
Conference
1063-6382
ISBN
Citations 
PageRank 
978-1-6654-0884-4
0
0.34
References 
Authors
23
6
Name
Order
Citations
PageRank
Geonmo Gu100.34
Yehyun Nam200.34
Kunsoo Park31396171.00
Zvi Galil400.34
Giuseppe F. Italiano500.34
Wook-Shin Han680557.85