Incremental Graph Pattern Matching Algorithm for Big Graph Data
Joint Authors
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-01-22
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Graph pattern matching is widely used in big data applications.
However, real-world graphs are usually huge and dynamic.
A small change in the data graph or pattern graph could cause serious computing cost.
Incremental graph matching algorithms can avoid recomputing on the whole graph and reduce the computing cost when the data graph or the pattern graph is updated.
The existing incremental algorithm PGC_IncGPM can effectively reduce matching time when no more than half edges of the pattern graph are updated.
However, as the number of changed edges increases, the improvement of PGC_IncGPM gradually decreases.
To solve this problem, an improved algorithm iDeltaP_IncGPM is developed in this paper.
For multiple insertions (resp., deletions) on pattern graphs, iDeltaP_IncGPM determines the nodes’ matching state detection sequence and processes them together.
Experimental results show that iDeltaP_IncGPM has higher efficiency and wider application range than PGC_IncGPM.
American Psychological Association (APA)
Zhang, Lixia& Gao, Jianliang. 2018. Incremental Graph Pattern Matching Algorithm for Big Graph Data. Scientific Programming،Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1214737
Modern Language Association (MLA)
Zhang, Lixia& Gao, Jianliang. Incremental Graph Pattern Matching Algorithm for Big Graph Data. Scientific Programming No. 2018 (2018), pp.1-8.
https://search.emarefa.net/detail/BIM-1214737
American Medical Association (AMA)
Zhang, Lixia& Gao, Jianliang. Incremental Graph Pattern Matching Algorithm for Big Graph Data. Scientific Programming. 2018. Vol. 2018, no. 2018, pp.1-8.
https://search.emarefa.net/detail/BIM-1214737
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1214737