A Survey of Relational Approaches for Graph Pattern Matching over Large Graphs

Due to rapid growth of the Internet and new scientific/technological advances, there exist many new applications that model data as graphs, because graphs have sufficient expressiveness to model complicated structures. The dominance of graphs in real-world applications demands new graph processing techniques to access and analyze large graph datasets effectively and efficiently. Among those techniques, a graph pattern matching problem receives increasing attention, which is to find all patterns in a large data graph that match a user-given graph pattern. In this survey, we review approaches to process such graph pattern queries with a framework of multi joins, which can be easily implemented in relational databases and requires no specialized native storage for graphs. We also discuss the top-k graph pattern matching problem.

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Continuous matching of evolving patterns over dynamic graph data

World Wide Web ◽

10.1007/s11280-020-00860-5 ◽

2021 ◽

Author(s):

Qianzhen Zhang ◽

Deke Guo ◽

Xiang Zhao ◽

Xi Wang

Keyword(s):

Pattern Matching ◽

Wide Spectrum ◽

Dynamic Graph ◽

Matching Problem ◽

Graph Pattern Matching ◽

Graph Data ◽

Graph Pattern ◽

Matching Process ◽

Data Graph ◽

Pattern Graph

AbstractNowadays, the scale of various graphs soars rapidly, which imposes a serious challenge to develop processing and analytic algorithms. Among them, graph pattern matching is the one of the most primitive tasks that find a wide spectrum of applications, the performance of which is yet often affected by the size and dynamicity of graphs. In order to handle large dynamic graphs, incremental pattern matching is proposed to avoid re-computing matches of patterns over the entire data graph, hence reducing the matching time and improving the overall execution performance. Due to the complexity of the problem, little work has been reported so far to solve the problem, and most of them only solve the graph pattern matching problem under the scenario of the data graph varying alone. In this article, we are devoted to a more complicated but very practical graph pattern matching problem, continuous matching of evolving patterns over dynamic graph data, and the investigation presents a novel algorithm for continuously pattern matching along with changes of both pattern graph and data graph. Specifically, we propose a concise representation of partial matching solutions, which can help to avoid re-computing matches of the pattern and speed up subsequent matching process. In order to enable the updates of data graph and pattern graph, we propose an incremental maintenance strategy, to efficiently maintain the intermediate results. Moreover, we conceive an effective model for estimating step-wise cost of pattern evaluation to drive the matching process. Extensive experiments verify the superiority of .

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Top-k graph pattern matching over large graphs

2013 IEEE 29th International Conference on Data Engineering (ICDE) ◽

10.1109/icde.2013.6544895 ◽

2013 ◽

Cited By ~ 3

Author(s):

Jiefeng Cheng ◽

Xianggang Zeng ◽

J. X. Yu

Keyword(s):

Pattern Matching ◽

Large Graphs ◽

Graph Pattern Matching ◽

Graph Pattern

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Multi-Fuzzy-Objective Graph Pattern Matching with Big Graph Data

Journal of Database Management ◽

10.4018/jdm.2019100102 ◽

2019 ◽

Vol 30 (4) ◽

pp. 24-40

Author(s):

Lei Li ◽

Fang Zhang ◽

Guanfeng Liu

Keyword(s):

Pattern Matching ◽

Particle Swarm Optimization Algorithm ◽

Nsga Ii ◽

Specific Domain ◽

Graph Pattern Matching ◽

Graph Data ◽

Graph Pattern ◽

Big Graph ◽

Complex Relationships ◽

Pattern Graph

Big graph data is different from traditional data and they usually contain complex relationships and multiple attributes. With the help of graph pattern matching, a pattern graph can be designed, satisfying special personal requirements and locate the subgraphs which match the required pattern. Then, how to locate a graph pattern with better attribute values in the big graph effectively and efficiently becomes a key problem to analyze and deal with big graph data, especially for a specific domain. This article introduces fuzziness into graph pattern matching. Then, a genetic algorithm, specifically an NSGA-II algorithm, and a particle swarm optimization algorithm are adopted for multi-fuzzy-objective optimization. Experimental results show that the proposed approaches outperform the existing approaches effectively.

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