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Scientific Programming
Volume 2018 (2018), Article ID 6749561, 8 pages
Research Article

Incremental Graph Pattern Matching Algorithm for Big Graph Data

1College of Mathematics and Computer Science, Key Laboratory of High Performance Computing and Stochastic Information Processing, Ministry of Education of China, Hunan Normal University, Changsha 410081, China
2School of Information Science and Engineering, Central South University, Changsha 410083, China

Correspondence should be addressed to Jianliang Gao; nc.ude.usc@gnailnaijoag

Received 19 October 2017; Accepted 20 December 2017; Published 22 January 2018

Academic Editor: Longxiang Gao

Copyright © 2018 Lixia Zhang and Jianliang Gao. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.