A Trust Network Model Based on Hesitant Fuzzy Linguistic Term Sets

Jieyu Zhan, Yuncheng Jiang, Wenjun Ma, Xudong Luo, Weiru Liu

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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Trust evaluation in a network is important in many areas, such as group decision-making and recommendation in e-commerce. Hence, researchers have proposed various trust network models, in which each agent rates the trustworthiness of others. Most of the existing work require the agents to provide accurate degrees of trust and distrust in advance. However, humans usually hesitate to choose one among several values to assess the trust in another person and tend to express the trust through linguistic descriptions. Hence, this paper proposes a novel trust network model that takes linguistic expression of trust into consideration. More specifically, we structure trust scores based on hesitant fuzzy linguistic term sets and give a comparison method. Moreover, we propose a trust propagation method based on the concept of computing with words to deal with trust relationships between indirectly connected agents, and such a method satisfies some intuitive properties of trust propagation. Finally, we conrm the advantages of our model by comparing it with related work.
Original languageEnglish
Title of host publicationInternational Conference on Knowledge Science, Engineering and Management
Subtitle of host publicationKSEM 2019: Knowledge Science, Engineering and Management
EditorsChristos Douligeris, Dimitris Karagiannis
PublisherSpringer Nature
Number of pages14
ISBN (Electronic)978-3-030-29563-9
ISBN (Print)978-3-030-29562-2
Publication statusPublished - 22 Aug 2019

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Nature
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • Trust network
  • Trust propagation
  • Hesitant fuzzy linguistic term sets
  • Concatenation operator
  • Aggregation operator


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