Incremental Graph Pattern Matching Algorithm for Big Graph Data

Abstract

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.