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

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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Multi-fuzzy-constrained graph pattern matching with big graph data

Intelligent Data Analysis ◽

10.3233/ida-194653 ◽

2020 ◽

Vol 24 (4) ◽

pp. 941-958

Author(s):

Guliu Liu ◽

Lei Li ◽

Xindong Wu

Keyword(s):

Pattern Matching ◽

Graph Pattern Matching ◽

Graph Data ◽

Graph Pattern ◽

Big Graph

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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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Multi-fuzzy-objective graph pattern matching in big graph environments with reliability, trust and social relationship

World Wide Web ◽

10.1007/s11280-019-00714-9 ◽

2019 ◽

Vol 23 (1) ◽

pp. 649-669

Author(s):

Lei Li ◽

Fang Zhang ◽

Zan Zhang ◽

Peipei Li ◽

Chenyang Bu

Keyword(s):

Pattern Matching ◽

Social Relationship ◽

Graph Pattern Matching ◽

Graph Pattern ◽

Big Graph

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Multiple Pattern Graph Correlations for Efficient Graph Pattern Matching

2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA) ◽

10.1109/aiccsa.2017.174 ◽

2017 ◽

Author(s):

Jing Yu ◽

Xiaomei Liu ◽

Yanbing Liu ◽

Yue Hu

Keyword(s):

Pattern Matching ◽

Graph Pattern Matching ◽

Graph Pattern ◽

Pattern Graph

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Incremental Graph Pattern Matching Algorithm for Big Graph Data

Scientific Programming ◽

10.1155/2018/6749561 ◽

2018 ◽

Vol 2018 ◽

pp. 1-8 ◽

Cited By ~ 5

Author(s):

Lixia Zhang ◽

Jianliang Gao

Keyword(s):

Pattern Matching ◽

Graph Matching ◽

Incremental Algorithm ◽

Graph Pattern Matching ◽

Application Range ◽

Graph Pattern ◽

Big Data Applications ◽

Data Graph ◽

Small Change ◽

Pattern Graph

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.

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