Incremental Graph Pattern Matching Algorithm for Big Graph Data

Lixia Zhang,Jianliang Gao

doi:10.1155/2018/6749561

Lixia Zhang, Jianliang Gao

Open Access

https://doi.org/10.1155/2018/6749561

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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.

Highlights

Graph pattern matching is to find all the subgraphs that are the same or similar to a given pattern graph P in a data graph G
We proposed an incremental graph matching algorithm named PGC IncGPM, which can be used in scenarios where data graphs are constant and pattern graphs are updated [13]
Snew(u) = snew(u) ∪ {w}; (21) repeatly filter sim(⋅) according to the parent and child relationships of nodes in the subgraph constructed by sim(⋅) to get added mat(⋅) and updated sim(⋅) and cand(⋅); Algorithm 1: iDeltaP IncGPM for edge deletions

Summary

Introduction

Graph pattern matching is to find all the subgraphs that are the same or similar to a given pattern graph P in a data graph G. Assuming that (B, E) and (C, D) are removed from the pattern graph, the traditional recomputing algorithm will compute the matches for the new pattern graph on the whole data graph. The study of incremental graph pattern matching is still in its infancy and existing work [6,7,8,9,10,11,12] mainly focuses on the updates of data graphs. We proposed an incremental graph matching algorithm named PGC IncGPM, which can be used in scenarios where data graphs are constant and pattern graphs are updated [13].

Related Work

Model and Definition

Experiments and Results Analysis

Conclusion