A consensus model for large-scale multi-attribute group decision making with collaboration-reference network under uncertain linguistic environment

Abstract

Social network (SN) provides a new perspective for large-scale multi-attribute group decision making (LMAGDM), and the scale and complexity of group compositions have received considerable attention. In recent study, the SN is constructed artificially and subjectively by using the number of communication or by giving the trust value directly. This paper constructs a directed and weighted SN by integrating collaboration network and reference network of decision makers (DMs) objectively. The spin-glass of community detection method is used to identify the subgroups and the weight of DMs in subgroups and then obtain the weight of subgroup pair. The uncertain linguistic weighted average operator is used to represent each subgroup’s assessment. The closeness between two subgroups is defined to measure consensus level. A targeted local feedback mechanism with three identification rules and a recommendation rule is designed to guide the consensus reaching process (CRP) more precisely and effectively. An illustrative example proves the feasibility and validity of the proposed consensus method, and the comparative analysis highlights the advantages and characteristics of this model.

Keywords

Large-scale multi-attribute group decision making collaboration-reference network social network weight of DMs and subgroup pair consensus model spin-glass community detection

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References

Liu ,

Liang ,

Chiclana and

Jian , A trust induced recommendation mechanism for reaching consensus in group decision making, Knowledge-Based Systems 119 (2017), 221–231.

Ureña ,

F.J.

Cabrerizo ,

J.A.

Morente-Molinera and

Herrera-Viedma , GDM-R: A new framework in R to support fuzzy group decision making processes, Information Sciences 357 (2016), 161–181.

M.A.S.J.

Mai , Multi-criteria group decision making based on neutrosophic analytic hierarchy process, Journal of Intelligent & Fuzzy Systems 33 (2017), 4055–4066.

D.C.

Morais and

A.T.

De Almeida , Group decision making on water resources based on analysis of individual rankings, Omega 40 (2012), 42–52.

Xu , Deviation measures of linguistic preference relations in group decision making, Omega 33 (2005), 249–254.

G.F.

Yu ,

D.F.

Li ,

J.M.

Qiu and

X.X.

Zheng , Some operators of intuitionistic uncertain 2-tuple linguistic variables and application to multi-attribute group decision making with heterogeneous relationship among attributes, Journal of Intelligent & Fuzzy Systems 34 (2018), 599–611.

Palomares ,

Martinez and

Herrera , A consensus model to detect and manage noncooperative behaviors in large-scale group decision making, IEEE Transactions on Fuzzy Systems 22 (2014), 516–530.

J.A.

Morente-Molinera ,

Kou ,

Peng ,

Torres-Albero and

Herrera-Viedma , Analysing discussions in social networks using group decision making methods and sentiment analysis, Information Sciences 447 (2018), 157–168.

Palomares ,

Martínez and

Herrera , MENTOR: A graphical monitoring tool of preferences evolution in large-scale group decision making, Knowledge-Based Systems 58 (2014), 66–74.

10.

X.H.

Chen and

Liu , Improved clustering algorithm and its application in complex huge group decision-making, Systems Engineering & Electronics 28 (2006), 1695–1699.

11.

Liu ,

Z.P.

Fan and

Zhang , A method for large group decision-making based on evaluation information provided by participators from multiple groups, Information Fusion 29 (2016), 132–141.

12.

Tong ,

Liu and

Qin , A linguistic solution for double large-scale group decision-making in E-commerce, Computers & Industrial Engineering 116 (2018), 97–112.

13.

Wu and

X.W.

Liu , An interval type-2 fuzzy clustering solution for large-scale multiple-criteria group decision-making problems, Knowledge-Based Systems 114 (2016), 118–127.

14.

Wu and

Xu , A consensus model for large-scale group decision making with hesitant fuzzy information and changeable clusters, Information Fusion 41 (2017), 217–231.

15.

Gou ,

Xu and

Herrera , Consensus Reaching Process for Large-scale Group Decision Making with Double Hierarchy Hesitant Fuzzy Linguistic Preference Relations, Knowledge-Based Systems 157 (2018), 20–33.

16.

Xu ,

Wen and

Zhang , A two-stage consensus method for large-scale multi-attribute group decision making with an application to earthquake shelter selection, Computers & Industrial Engineering 116 (2018), 113–129.

17.

A.J.T.

Lee ,

F.C.

Yang ,

H.C.

Tsai and

Y.Y.

Lai , Discovering content-based behavioral roles in social networks, Decision Support Systems 59 (2014), 250–261.

18.

Jian ,

Chiclana ,

Fujita and

Herrera-Viedma , A visual interaction consensus model for social network group decision making with trust propagation, Knowledge-Based Systems 122 (2017), 39–50.

19.

Jian ,

Chiclana and

Herrera-Viedma , Trust based consensus model for social network in an incomplete linguistic information context, Applied Soft Computing 35 (2015), 827–839.

20.

Tong ,

Liu and

Fang , An interval type-2 fuzzy TOPSIS model for large scale group decision making problems with social network information, Information Sciences 432 (2018), 392–410.

21.

French , Web-enabled strategic GDSS, democracy and Arrow’s theorem: A Bayesian perspective, Decision Support Systems 43 (2007), 1476–1484.

22.

Qian ,

Liao and

Liu , A, Knowledge-Based Systems 119 (2016), 68–86.

23.

Li ,

Dong ,

Peng and

Jiang , Interpreting the formation of co-author networks via utility analysis, Information Processing & Management 53 (2017), 624–639.

24.

Nykl ,

Ježek ,

Fiala and

Dostal , PageRank variants in the evaluation of citation networks, Journal of Informetrics 8 (2014), 683–692.

25.

Yang , L.B.T.-I.C. on A.C.T. &

Yun , Literature recommendation based on reference graph, in: International Conference on Advanced Computer Theory and Engineering, 2010, pp. V3400–V403-404.

26.

S.Y.

Hwang ,

C.P.

Wei and

Y.F.

Liao , Coauthorship networks and academic literature recommendation, Electronic Commerce Research & Applications 9 (2010), 323–334.

27.

Yu ,

Liu ,

Yang ,

Chen ,

Jiang ,

Tolba and

Xia , PAVE: Personalized academic venue recommendation Exploiting co-publication networks, Journal of Network and Computer Applications 104 (2018), 38–47.

28.

Sun and

Rahwan , Coauthorship network in transportation research, Transportation Research Part A Policy & Practice 100 (2017), 135–151.

29.

Samitas and

Kampouris , Empirical investigation of co-authorship in the field of finance: A network perspective, International Review of Financial Analysis (2017).

30.

Li and

Li , B.W.B.T.-I.C. on C.S. &

Liu , Decipher the Hidden Secrets Behind Networks: Analyze the Influence Based on the Co-author Network, in: International Conference on Computer Science and Applications, 2017, pp. 323–328.

31.

Xie ,

Ouyang and

Li , A geometric graph model for coauthorship networks, Journal of Informetrics 10 (2016), 299–311.

32.

Herrera and

Martinez , A model based on linguistic 2-tuples for dealing with multigranular hierarchical linguistic contexts in multi-expert decision-making, IEEE Transactions on Systems Man & Cybernetics Part B Cybernetics A Publication of the IEEE Systems Man & Cybernetics Society 31 (2001), 227–234.

33.

Herrera and

Martinez , An approach for combining linguistic and numerical information based on the 2-tuple fuzzy linguistic representation model in decision-making, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 8 (2000), 539–562.

34.

Xu , A method based on linguistic aggregation operators for group decision making with linguistic preference relations, Information Sciences 166 (2004), 19–30.

35.

Xu , Induced uncertain linguistic OWA operators applied to group decision making, Information Fusion 7 (2006), 231–238.

36.

L.A.

Zadeh , The concept of a linguistic variable and its application to approximate reasoning, Information Sciences 8 (1975), 199–249.

37.

Herrera ,

Herrera-Viedma and

J.L.

Verdegay , A sequential selection process in group decision making with a linguistic assessment approach, Information Sciences 85 (1995), 223–239.

38.

Herrera , Viedma and

L.J.

Verdegay , A model of consensus in group decision making under linguistic assessments, Fuzzy Sets & Systems 78 (1996), 73–87.

39.

Herrera ,

Herrera-Viedma and

MartíNez , A fusion approach for managing multi-granularity linguistic term sets in decision making, Fuzzy Sets & Systems 114 (2000), 43–58.

40.

Xu and

Da , A method for multiple attribute decision making with incomplete weight information under uncertain linguistic environment, Knowledge-Based Systems 21 (2008), 837–841.

41.

Pang ,

Liang and

Peng , An adaptive consensus method for multi-attribute group decision making under uncertain linguistic environment, Applied Soft Computing 58 (2017), 339–353.

42.

Xu , An approach based on the uncertain LOWG and induced uncertain LOWG operators to group decision making with uncertain multiplicative linguistic preference relations, Decision Support Systems 41 (2006), 488–499.

43.

Wu and

Chiclana , A social network analysis trust-consensus based approach to group decision-making problems with interval-valued fuzzy reciprocal preference relations, Knowledge-Based Systems 59 (2014), 97–107.

44.

Gou and

Xu , Novel basic operational laws for linguistic terms, hesitant fuzzy linguistic term sets and probabilistic linguistic term sets, Information Sciences 372 (2016), 407–427.

45.

Son and

S.B.

Kim , Academic paper recommender system using multilevel simultaneous citation networks, Decision Support Systems 105 (2018), 24–33.

46.

M.E.J.

Newman and

Girvan , Finding and evaluating community structure in networks, Physical Review E Statistical Nonlinear & Soft Matter Physics 69 (2004), 26113.

47.

Pan and

Wang , JBT-IIC on, T. with,

A.I.

Xie and

Spin-Glass , model based local community detection method in social networks, IEEE Transactions on Neural Networks 11 (2013), 1490–1497.

48.

JöRg and

Stefan , Statistical mechanics of community detection, Physical Review E Statistical Nonlinear & Soft Matter Physics 74 (2006), 16110.

49.

Wang ,

Liu ,

Liu and

Pan , Fuzzy overlapping community detection based on local random walk and multidimensional scaling, Physica A Statistical Mechanics & Its Applications 392 (2013), 6578–6586.

50.

Sattari and

Zamanifar , A cascade information diffusion based label propagation algorithm for community detection in dynamic social networks, Journal of Computational Science 25 (2018), 122–133.

51.

R.R.

Yager , Quantifier guided aggregation using OWA operators, International Journal of Intelligent Systems 11 (1996), 49–73.

52.

Beliakov and

James , Induced ordered weighted averaging operators, IEEE Transactions on Systems Man & Cybernetics Part B Cybernetics A Publication of the IEEE Systems Man & Cybernetics Society 26 (2011), 866–886.