Utilizing Constraint Satisfaction Techniques for Efficient Graph Pattern Matching

Constraint satisfaction algorithms for graph pattern matching

Mathematical Structures in Computer Science ◽

10.1017/s0960129501003577 ◽

2002 ◽

Vol 12 (4) ◽

pp. 403-422 ◽

Cited By ~ 65

Author(s):

JAVIER LARROSA ◽

GABRIEL VALIENTE

Keyword(s):

Pattern Matching ◽

Constraint Satisfaction ◽

Formal Proof ◽

Additional Contribution ◽

Large Set ◽

Worst Case ◽

Graph Pattern Matching ◽

Graph Pattern ◽

Look Ahead ◽

Application Fields

Graph pattern matching is a central problem in many application fields. Since it is NP-complete, we cannot expect to find algorithms with a good worst-case performance. However, there is still room for general procedures with a good average performance. In this paper we explore four different solving approaches within the constraint satisfaction framework, and introduce a new algorithm, which we call nRF+. The algorithm is a refinement of really full look ahead that takes advantage of the problem structure in order to enhance the look ahead procedure. We give a formal proof that nRF+ is superior to the other approaches in terms of number of visited nodes. An additional contribution of this paper is the introduction of a new benchmark for testing algorithms in this domain. It is formed by a large set of well-defined graphs of very diverse nature. In this benchmark, we show that nRF+ can efficiently solve a broad range of problems, while still leaving many problem instances unsolved. The use of this challenging benchmark is encouraged for future algorithms evaluation.

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