Intuitionistic Fuzzy Requirements Aggregation for Graph Pattern Matching with Group Decision Makers

Abstract

Graph Pattern Matching (GPM) plays an important role in the field of multi-attribute decision making. By designing a pattern graph involving multiple attribute constraints of the Decision Maker (DM), the sub graphs can be matched from the data graph. However, the existing work rarely considers the requirements from group DMs. In this case, the requirements on each attribute have multiple values from different DMs. How to aggregate these requirements and perform efficient sub graph matching is a challenging task. In this paper, first, a sub graph query problem that needs to consider the multiple requirements from group DMs is proposed. Then, to solve this problem, a Multi-Requirement-based Sub graph Query model (MR-SQ) is proposed, which is mainly composed of two stages: group require-ments aggregation and GPM. For the first stage, an Intuitionistic Fuzzy Requirements Aggregation (IFRA) method is proposed for requirements aggregation. Then, to solve the efficiency problem of large-scale GPM, a parallel strategy is designed for the GPM stage. Finally, the practicability and effectiveness of the proposed model have been verified through an illustrative example and time- performance comparison experiments.