In the social network, trust information features dynamic and incomplete. The experts’ weight information is unknown beforehand, or there is not enough reliable sources. The dynamic process of trust propagation is analyzed, a new TS-UTOWA operator for multipath propagation is introduced, and an innovative D-UTOWA operator is proposed for trust aggregation in this paper. As for the efficiency of uninorm trust propagation, the average value of the other’s values is adopted to estimate the incomplete sociomatrix vector. Finally, a case about tourist route arrangment is discussed to demonstrate the efficiency and feasibility of the theory in this article.
AlonsoS., PérezI.J., CabrerizoF.J. and Herrera-ViedmaE., A linguistic consensus model for Web 2.0 communities, Applied Soft Computing13 (2013), 149–157.
BansalS.K., BansalA., BlakeM.B., Trust-based Dynamic Web service Composition using Social Network Analysis, IEEE International Workshop on Business Applications of Social Network Analysis, 2011, pp. 1–8.
4.
BazhenovV.A., PogorelovaO.S. and PostnikovaT.G., Contact impact forces at discontinuous 2-DOF vibroimpact, Applied Mathematics & Nonlinear Sciences1 (2016), 183–196.
5.
ChenS.M. and LeeL.W., Autocratic decision making using group recommendations based on the ILLOWA operator and likelihood-based comparison relations, South African Journal of Industrial Engineering26 (2012), 1–11.
6.
ChiclanaF., Herrera-ViedmaE., HerreraF. and AlonsoS., Some induced ordered weighted averaging operators and their use for solving group decision-making problems based on fuzzy preference relations, European Journal of Operational Research182 (2007), 383–399.
7.
CiucciD., Orthopairs: A simple and widely usedway to model uncertainty, Fundamenta Informaticae108 (2011), 287–304.
8.
De CockM. and SilvaP.P.D., A many valued representation and propagation of trust and distrust, in: International Conference on Fuzzy Logic and Applications, 2005, pp. 114–120
9.
DreweJ.A., EamesK.T., MaddenJ.R. and PearceG.P., Integrating contact network structure into tuberculosis epidemiology in meerkats in South Africa: Implications for control, Preventive Veterinary Medicine101 (2011), 113–120.
10.
HannemanR.A. and RiddleM., Introduction to Social Network Methods, Department of Sociology University of California Riverside (2005).
11.
Herrera-ViedmaE., CabrerizoF.J., KacprzykJ. and PedryczW., A review of soft consensus models in a fuzzy environment, Information Fusion17 (2014), 4–13.
12.
JohnS., Social network analysis a handbook, Contemporary Sociology22 (2000).
13.
KacprzykJ., FedrizziM. and NurmiH., Group decision making and consensus under fuzzy preferences and fuzzy majority, Fuzzy Sets & Systems49 (1992), 21–31.
14.
KaiserC., SchlickS. and BodendorfF., Warning system for online market research – Identifying critical situations in online opinion formation, Knowledge-Based Systems24 (2011), 824–836.
15.
LiB. and YouN., Research on the dynamic evolution behavior of group loitering air vehicles, Applied Mathematics & Nonlinear Sciences1 (2016), 353–359.
16.
PérezL.G., MataF. and ChiclanaF., Social network decision making with linguistic trustworthiness–based induced OWA operators, International Journal of Intelligent Systems29 (2014), 1117–1137.
17.
PappiF.U., Social network analysis: A Handbook. by John Scott, Contemporary Sociology22 (2000).
18.
Rez-AsurmendiP. and ChiclanaF., Linguistic majorities with difference in support, Applied Soft Computing18 (2014), 196–208.
19.
TakacC., HinzO. and SpannM., The social embeddedness of decision making: Opportunities and challenges, Electronic Markets21 (2011), 185–195.
20.
TorraV., Hesitant fuzzy sets, International Journal of Intelligent Systems25 (2010), 529–539.
21.
UreñaR., ChiclanaF., Morente-MolineraJ.A. and Herrera-ViedmaE., Managing incomplete preference relations in decision making: A review and future trends, Information Sciences302 (2015), 14–32.
22.
VictorP., CornelisC., CockM.D. and SilvaP.P.D., Gradual trust and distrust in recommender systems, Fuzzy Sets & Systems160 (2009), 1367–1382.
23.
WalterF.E., BattistonS., SchweitzerF., Personalised and dynamic trust in social networks, ACM Conference on Recommender Systems, 2009, pp. 197–204.
24.
WassermanB.S. and FaustK., Methods and Implications, Social Network Analysis, 2010.
25.
WassermanS. and FaustK., Social network analysis: Methods and applications, Contemporary Sociology91 (1994), 219–220.
26.
WellmanB., The Development of Social Network Analysis: A Study in the Sociology of Science by Linton C. Freeman, Study in the Sociology of Science (Empirical), 2004.
27.
WuJ. and ChiclanaF., Non-dominance and attitudinal prioritisation methods for intuitionistic and interval-valued intuitionistic fuzzy preference relations, Expert Systems with Applications39 (2012), 13409–13416.
28.
WuJ., ChiclanaF. and Herrera-ViedmaE., Trust based consensus model for social network in an incomplete linguistic information context, Applied Soft Computing35 (2015), 827–839.
29.
WuJ., XiongR. and ChiclanaF., Uninorm trust propagation and aggregation methods for group decision making in social network with four tuple information, Knowledge-Based Systems96 (2016), 29–39.
30.
XuZ. and YagerR.R., Some geometric aggregation operators based on intuitionistic fuzzy sets, International Journal of General Systems35 (2006), 417–433.
31.
XuZ. and CaiX., Dynamic intuitionistic fuzzy multi-attribute decision making, International Journal of Approximate Reasoning48 (2008), 246–262.
32.
YagerR.R., Quantifier guided aggregation using OWA operators, International Journal of Intelligent Systems11 (1996), 49–73.
33.
ZadehL.A., Fuzzy sets, Information & Control8 (1965), 338–353.
34.
ZadehL.A., A computational approach to fuzzy quantifiers in natural languages ✩, Computers & Mathematics with Applications9 (1983), 149–184.
