Social Network Large-Scale Decision-Making: Developing Decision Support Methods at Scale and Social Networks (Uncertainty and Operations Research)

Preface
Contents
About the Authors
Acronyms
Important Symbols
List of Figures
List of Tables
1 Introduction
	1.1 Motivation
	1.2 Who Should Read This Book and Why?
	1.3 Chapter Overview
2 Preliminary Knowledge
	2.1 Large-Scale Decision-Making (LSDM)
	2.2 Social Network Group Decision-Making (SNGDM)
	2.3 Consensus-Reaching Process and Minimum-Cost Consensus
	2.4 Proposed Social Network Large-Scale Decision-Making Scenarios
	References
3 Trust and Behavior Analysis-Based Structure-Heterogeneous Information Fusion
	3.1 Research Background and Problem Configuration
	3.2 Analysis of the Selection Behaviors of Attributes and Alternatives
	3.3 Procedure of Trust and Behavior Analysis-Based Fusion Method
		3.3.1 Constructing the Trust Sociomatrix
		3.3.2 Calculating the Distance Between SH Evaluation Information
		3.3.3 Generating the Weights of DMs
		3.3.4 Fusing SH Individual Evaluation Information
	3.4 Discussion and Comparative Analysis
		3.4.1 Further Analysis on the Calculation of the Weights of DMs
		3.4.2 Dealing with Extreme Decision Situations
		3.4.3 Comparative Analysis
	3.5 Conclusions
	References
4 Trust Cop-Kmeans Clustering Method
	4.1 Trust-Similarity Analysis-Based Decision Information Processing
	4.2 Trust Cop-Kmeans Clustering Algorithm
	4.3 Determining the Weights of Clusters and DMs
	4.4 Discussion and Comparative Analysis
		4.4.1 TCop-Kmeans Algorithm Versus K-Means Algorithm
		4.4.2 Determination of Trust Constraint Threshold
		4.4.3 Analysis of upper KK
	4.5 Conclusions
	References
5 Compatibility Distance Oriented Off-Center Clustering Method
	5.1 Preliminaries About PLTSs and Problem Configuration
		5.1.1 Probabilistic Linguistic Term Sets (PLTSs)
		5.1.2 Configuration of an SNLSDM-PL Problem
	5.2 Compatibility Distance Oriented Off-Center Clustering Algorithm
		5.2.1 CDOOC Clustering Algorithm
		5.2.2 Visualization of CDOOC Clustering Algorithm
		5.2.3 Generation of the Weights of Clusters
	5.3 Comparative Analysis and Discussion
		5.3.1 Comparison with Traditional Clustering Algorithms
		5.3.2 Analysis of qq
	5.4 Conclusions
	References
6 Minimum-Cost Consensus Model Considering Trust Loss
	6.1 Problem Configuration
	6.2 Consensus Measure and Consensus Cost Measure
	6.3 Consensus-Reaching Iteration Based on Improved MCC
	6.4 Numerical Experiment
	6.5 IMCC Model Versus Different MCC Models
	6.6 Analysis of the Effect of Voluntary Trust Loss on the CRP
	6.7 Conclusions
	References
7 Punishment-Driven Consensus-Reaching Model Considering Trust Loss
	7.1 Problem Configuration
	7.2 Computing the Consensus Degree
	7.3 Logic for Solving CRP Using Trust Loss
	7.4 Consensus Scenario Classification and Adjustment Strategies
	7.5 Analysis of the Moderating Effect of Trust Loss on the CRP
	7.6 Comparison with Other LSDM Consensus Models
	7.7 Conclusions
	References
8 Practical Applications
	8.1 Application of TBA-Based Information Fusion Method in Coal …
		8.1.1 Case Description
		8.1.2 Decision Process
	8.2 Application of PDCRM in Social Capital Selection
		8.2.1 Case Description
		8.2.2 Using PDCRM to Solve the Problem
	8.3 Application of CDOOC Clustering Method in Car-Sharing …
	References
9 Conclusions and Future Research Directions
	9.1 Findings and Conclusions
	9.2 Future Research Directions