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A review on trust propagation and opinion dynamics in social networks and group decision making frameworks

期刊

INFORMATION SCIENCES
卷 478, 期 -, 页码 461-475

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2018.11.037

关键词

Trust; Reputation; Influence; Social networks; Decision making; Opinion dynamics

资金

  1. EU [H2020-MSCA-IF-2016-DeciTrustNET-746398]
  2. FEDER funds [TIN2016-75850-P]
  3. RUDN University Program 5-100 (Russian Federation)

向作者/读者索取更多资源

On-line platforms foster the communication capabilities of the Internet to develop large-scale influence networks in which the quality of the interactions can be evaluated based on trust and reputation. So far, this technology is well known for building trust and harnessing cooperation in on-line marketplaces, such as Amazon (www.amazon.com) and eBay (www.ebay.es). However, these mechanisms are poised to have a broader impact on a wide range of scenarios, from large scale decision making procedures, such as the ones implied in e-democracy, to trust based recommendations on e-health context or influence and performance assessment in e-marketing and e-learning systems. This contribution surveys the progress in understanding the new possibilities and challenges that trust and reputation systems pose. To do so, it discusses trust, reputation and influence which are important measures in networked based communication mechanisms to support the worthiness of information, products, services opinions and recommendations. The existent mechanisms to estimate and propagate trust and reputation, in distributed networked scenarios, and how these measures can be integrated in decision making to reach consensus among the agents are analysed. Furthermore, it also provides an overview of the relevant work in opinion dynamics and influence assessment, as part of social networks. Finally, it identifies challenges and research opportunities on how the so called trust based network can be leveraged as an influence measure to foster decision making processes and recommendation mechanisms in complex social networks scenarios with uncertain knowledge, like the mentioned in e-health and e-marketing frameworks. (C) 2019 The Authors. Published by Elsevier Inc.

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