Incremental Graph Pattern Matching Algorithm for Big Graph Data

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.

Download Full-text

Graph Pattern Matching: Capturing Bisimilar Subgraph

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001421500117 ◽

2020 ◽

pp. 2150011

Author(s):

Rui Qiao ◽

Ke Feng ◽

Heng He ◽

Xiaolei Zhong

Keyword(s):

Pattern Matching ◽

Graph Matching ◽

Directed Graphs ◽

Graph Databases ◽

Graph Pattern Matching ◽

Query Graph ◽

Graph Pattern ◽

Finite Directed Graphs ◽

Data Graph ◽

Text Language

Graph pattern matching that aims to seek out answer graphs in a data graph matching a provided graph, plays a fundamental role as a part of graph search for graph databases. “Matching” indicates that the two graphs are correlated, such as bisimulation, isomorphism, simulation, etc. The strictness of bisimulation is between simulation and isomorphism. Seldom work has been done to search for bisimulation subgraphs. This research focuses on the problem. The symbol [Formula: see text] is introduced to fundamental modal logic language, thereby yielding [Formula: see text] language; the symbols [Formula: see text] is added for forming [Formula: see text] formulas. Then conclusions about graph bisimulations are shown. Subsequently, a theorem with detailed proof is presented, stating that [Formula: see text] formulas characterize finite directed graphs modulo bisimulation. According to the conclusions and theorem, algorithms for finding subgraphs are proposed. After dividing the query graph, the match graphs undergo the characterization using [Formula: see text] formulas. In the data graphs, by model checking the formulas, the answer graphs exhibiting bisimilarity to the match graphs are able to be captured.

Download Full-text

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 .

Download Full-text

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.

Download Full-text

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

Download Full-text

Graph-Based Node Finding in Big Complex Contextual Social Graphs

Complexity ◽

10.1155/2020/7909826 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Keshou Wu ◽

Guanfeng Liu ◽

Junwen Lu

Keyword(s):

Social Trust ◽

Graph Matching ◽

Search Algorithm ◽

Social Contexts ◽

Graph Pattern Matching ◽

Monte Carlo Tree Search ◽

Graph Pattern ◽

The Social ◽

Efficiency And Effectiveness ◽

Pattern Graph

Graph pattern matching is to find the subgraphs matching the given pattern graphs. In complex contextual social networks, considering the constraints of social contexts like the social relationships, the social trust, and the social positions, users are interested in the top-K matches of a specific node (denoted as the designated node) based on a pattern graph, rather than the entire set of graph matching. This inspires the conText-Aware Graph pattern-based top-K designated node matching (TAG-K) problem, which is NP-complete. Targeting this challenging problem, we propose a recurrent neural network- (RNN-) based Monte Carlo Tree Search algorithm (RN-MCTS), which automatically balances exploring new possible matches and extending existing matches. The RNN encodes the subgraph and maps it to a policy which is used to guide the MCTS. The experimental results demonstrate that our proposed algorithm outperforms the state-of-the-art methods in terms of both efficiency and effectiveness.

Download Full-text