Welcome to the InfoSci Platform
Reference Hub6
Multi-Fuzzy-Objective Graph Pattern Matching with Big Graph Data

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

Lei Li, Fang Zhang, Guanfeng Liu
Copyright: © 2019 |Volume: 30 |Issue: 4 |Pages: 17
ISSN: 1063-8016|EISSN: 1533-8010|EISBN13: 9781522563815|DOI: 10.4018/JDM.2019100102
Cite Article Cite Article

MLA

Li, Lei, et al. "Multi-Fuzzy-Objective Graph Pattern Matching with Big Graph Data." JDM vol.30, no.4 2019: pp.24-40. http://doi.org/10.4018/JDM.2019100102

APA

Li, L., Zhang, F., & Liu, G. (2019). Multi-Fuzzy-Objective Graph Pattern Matching with Big Graph Data. Journal of Database Management (JDM), 30(4), 24-40. http://doi.org/10.4018/JDM.2019100102

Chicago

Li, Lei, Fang Zhang, and Guanfeng Liu. "Multi-Fuzzy-Objective Graph Pattern Matching with Big Graph Data," Journal of Database Management (JDM) 30, no.4: 24-40. http://doi.org/10.4018/JDM.2019100102

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.