Abstract
The reality of group decision problem is often a complex large group problem, and because of the influence of the factors such as personality, psychology, values and connection degree, group members can form different community organizations. The structure of community organizations, especially the division of the recessive community organizations and its structure in the group has a significant influence on the decision results. In this paper, under the perspective of complex network, the relationship between members of the group decision was abstracted as the weighted network, applying the agglomerative algorithm idea of nodes similarity, using the theory and method of the community partition of complex network, a community partition algorithm for the node empower network based on the measure the similarity of nodes is designed and verified. The algorithm considers both the properties and structural characteristics of nodes in the network, respectively reflects the individual’s knowledge level and communication network in group decision-making, which can be used to search for the recessive organization of group decision-making, laid a foundation to simulate group evolution process and the decision results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen X, Xu X, Zeng J (2007) Method of multi-attribute large group decision making based on entropy weight. Syst Eng Electron 7:1305–1309
Chiclana F, García J et al (2013) A statistical comparative study of different similarity measures of consensus in group decision making. Inf Sci 221:110–123
Chuu S (2009) Selecting the advanced manufacturing technology using fuzzy multiple attributes group decision making with multiple fuzzy information. Comput Ind Eng 57:1033–1042
Galin A (2013) Endowment effect in negotiations: group versus individual decision making. Theor Decis 75(3):389–401
He H, Martinsson P, Sutter M (2012) Group decision making under risk: an experiment with student couples. Econ Lett 117(3):691–693
Hummel P (2012) Sequential voting in large elections with multiple candidates. J Public Econ 96(3–4):341–348
Intepe G, Bozdag E, Koc T (2013) The selection of technology forecasting method using a multi-criteria interval-valued intuitionistic fuzzy group decision making approach. Comput Ind Eng 65(2):277–285
Kaplan T, Forrest S (2008) A dual assortative measure of community structure. E Print arXiv 0801:3290
Karrer B, Levina E, Newman M (2008) Robustness of community structure in networks. Phys Rev E 77(046):119
Leicht E, Holme P, Newman M (2006) Vertex similarity in networks. Phys Rev E 73(2):026120
Liu Y, Wei X, Chen X (2011) The effects of group emotional intelligence on group decision-making behaviors and outcomes. J Manage Sci China 10:11–27
Lu J, Xu Y, An L (2012) A partitioning method for community structure in weighted networks based on node similarity. Inf Control 08:15
Lu Z (2013) Transition and evolutionof complex group decision-making process. Chin J Syst Sci 1:62–65
Nadakuditi R, Newman M (2012) Graph spectra and the detectability of community structure in networks. Phys Rev Lett 108(188):701
Newman M (2004) Analysis of weighted networks. Phys Rev E 70(056):131
Newman M (2004) Detecting community structure in networks. Eur Phys J B 38:321–330
Newman M (2004) Fast algorithm for detecting community structure in networks. Phys Rev E 69(6):066133
Newman M (2006) Modularity and community structure in networks. Proc Nat Acad Sci USA 103:8577–8582
Newman M (2012) Communities, modules and large-scale structure in networks. Nat Phys 8:25–31
Pan Y, Li D, Liu G et al (2010) Detecting community structure in complex networks via node similarity. Physica A 389(14):2849–2857
Tsuchiura H, Ogata M, et al. (2003) Electronic states around a vortex core in high-TC superconductors based on the TJ model. Phys Rev B 68(1):012509
Wang L (2008) Collective decision-making over complex networks. Caai Trans Intell Syst 3(2):95–108
Wang X, Li X, Cheng G (2011) Complex networks theory and its application. Tsinghua University Press, Beijing
Yang L, Guo M (2011) Opinion convergence analysis of interaction among different subgroups in dynamic group decision making. Ind Eng J 14(4):104–109
Yu H, Liu Z, Li Y (2013) Key nodes in complex networks identified by multi-attribute decision-making method. Acta Phys Sinica 62(2):1–9
Zachary W (1977) An information flow model for conflict and fission in small groups. J Anthropol Res 33:452–473
Zhang C, Shen H (2013) Modularity for community structure in complex network based on spectral method. Syst Eng-Theory Pract 33(5):1232–1240
Acknowledgments
This work was partly supported by the National Natural Science Funds of China (Project No. 71071102), and the Fundamental Research Funds for the Central Universities (No. skqy201324).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Guo, C., Shi, R., Zhou, Z. (2014). The Identification of Recessive Community Organization in Group Decision Making. In: Xu, J., Cruz-Machado, V., Lev, B., Nickel, S. (eds) Proceedings of the Eighth International Conference on Management Science and Engineering Management. Advances in Intelligent Systems and Computing, vol 280. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55182-6_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-55182-6_22
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-55181-9
Online ISBN: 978-3-642-55182-6
eBook Packages: EngineeringEngineering (R0)