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

2021 IEEE International Conference on Big Knowledge (ICBK) Pub Date : 2021-12-01 DOI:10.1109/ICKG52313.2021.00023

Haixia Zhao, Guliu Liu, Lei Li, Jiao Li

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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.

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面向群体决策者的图模式匹配直觉模糊需求聚合

图模式匹配(GPM)在多属性决策领域发挥着重要作用。通过设计包含多个属性约束的模式图，可以从数据图中匹配子图。然而，现有的工作很少考虑来自组dm的需求。在这种情况下，每个属性的需求具有来自不同dm的多个值。如何聚合这些需求并执行有效的子图匹配是一项具有挑战性的任务。本文首先提出了一种需要考虑群dm多个需求的子图查询问题。然后，针对这一问题，提出了一种基于多需求的子图查询模型(MR-SQ)，该模型主要由组需求聚合和GPM两个阶段组成。第一阶段，提出了一种直观模糊需求聚合(IFRA)方法。然后，为解决大规模GPM的效率问题，设计了GPM阶段的并行策略。最后，通过一个算例和时间性能对比实验，验证了所提模型的实用性和有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2021 IEEE International Conference on Big Knowledge (ICBK)

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