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

Lei Li*, Fang Zhang, Guanfeng Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


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 multifuzzy-objective optimization. Experimental results show that the proposed approaches outperform the existing approaches effectively.

Original languageEnglish
Pages (from-to)24-40
Number of pages17
JournalJournal of Database Management
Issue number4
Publication statusPublished - Oct 2019


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


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