A signed network analysis-based Consensus Reaching Process in Group Decision Making

Zhang, Zi-xuan; Xu, You-wei; Hao, Wenning; Yu, Xiaohan

doi:10.1016/j.asoc.2020.106926

Cited by 11 publications

(6 citation statements)

References 42 publications

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“…According to different calculation methods of the weights of decision-makers, GDM problems can be divided into two categories: homogeneous and heterogeneous (Herrera and Herrera-Viedma, 1997;Chiclana et al, 2006). Homogeneity means that decision-makers are equally important, while heterogeneity requires us to calculate the additional importance of decision-makers (Zi-xuan et al, 2021). In general, homogeneity does not exist in trust networks, and standardized in-degree centrality is a commonly used indicator to measure the importance of decision-makers.…”

Section: The Importance Of Decision-makersmentioning

confidence: 99%

“…Therefore, how to reach consensus among decision-makers has become a core issue. Many studies have incorporated consensus reaching process (CRP) into GDM framework to facilitate decision-makers to reach an acceptable consensus (Parreiras et al, 2012;Ureña et al, 2018a;Zi-xuan et al, 2021). In early multi-criteria decisionmaking (MCDM) problems, most decision-makers can quantitatively evaluate alternatives through the modeling of various fuzzy sets.…”

Section: Introductionmentioning

confidence: 99%

“…In the field of fuzzy measure, well-known methods to describe interaction phenomena include M€ obius representation, Shapley and Banzhaf simultaneous interaction indices (Grabisch and Roubens, 1999;Fujimoto et al, 2005). On this basis, Zi-xuan et al (2021) put forward the concept of comprehensive nonmodular index and discussed its theoretical relationships with traditional interaction index. Interaction index reflects the average degree of synergy between people, which is similar to Shapley index in game theory (Cai et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…, 2005). On this basis, Zi-xuan et al (2021) put forward the concept of comprehensive nonmodular index and discussed its theoretical relationships with traditional interaction index. Interaction index reflects the average degree of synergy between people, which is similar to Shapley index in game theory (Cai et al.…”

Section: Introductionmentioning

confidence: 99%

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A trust-enhanced consensus reaching model based on interaction among decision-makers with incomplete preferences

Jian

Cai

et al. 2022

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PurposeThe development of social networks enhances the interaction between people, which brings new challenges to the research of group decision-making (GDM). This study aims at the problem that the synergy and redundancy due to interaction among decision-makers are ignored in the previous GDM, a trust-enhanced consensus reaching model based on interaction among decision-makers with incomplete preferences is proposed.Design/methodology/approachFirstly, confidence level is introduced to improve the hesitation phenomenon that should be considered when calculating trust degree; Secondly, a new trust propagation operator is developed to deal with indirect trust relationships; Thirdly, trust degree is transformed into interaction index to quantify the synergy and redundancy in decision-making. Fuzzy capacities of decision-makers are used to replace traditional weights, and the final scores of alternatives are obtained through Choquet integral.FindingsThe proposed model using fuzzy capacity can reflect the synergy or redundancy among decision-makers and improve the accuracy of final ranking result and reduce the loss of information.Originality/valueThis study proposes a trust-enhanced consensus reaching model, which develops a new trust propagation operator to ensure the continuous attenuation of trust in propagation process. And the proposed model uses fuzzy capacity to improve the enhancement or attenuation on the scores of alternatives.

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Section: The Importance Of Decision-makersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A trust-enhanced consensus reaching model based on interaction among decision-makers with incomplete preferences

Jian

Cai

et al. 2022

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“…Many real-world systems from various fields of research can be represented by graphs, including communication networks, social networks [50,51], protein-protein interaction networks [52], and chemistry molecular graphs [53]. In other words, graphs can be viewed as a special kind of data structure that models the pairwise relationships (edges) between a set of objects (nodes).…”

Section: Graph Convolution Neural Networkmentioning

confidence: 99%

A Graph Convolution Neural Network-Based Framework for Communication Network K-Terminal Reliability Estimation

Zhang

Cao³

et al. 2022

Security and Communication Networks

Self Cite

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The exact computation of network k-terminal reliability is an NP-hard problem, and many approximation methods have been proposed as alternatives, among which the neural network-based approaches are believed to be the most effective and promising. However, the existing neural network-based methods either ignore the local structures in the network topology or process the local structures as Euclidean data, while the network topology represented by the graph is in fact non-Euclidean. Seeing that the Graph Convolution Neural network (GCN) is a generalization of convolution operators onto non-Euclidean data structure, in an effort to fill in the gap, this paper proposes a GCN-based framework for the estimation of communication network reliability. First, a dataset with sufficient sample size is constructed, by calculating the k-terminal reliability via the exact contraction-deletion method for the generated network samples. Then, an estimation model based on GCN is built, where several graph convolution layers process input information and extract node-level structural features from the network topology, a concatenation layer fuses the structural features into a graph-level representation feature, and a multi-layer perceptron computes the k-terminal reliability as output. To demonstrate the practicality and rationality of our proposed model, comparative experiments are carried out on 12 datasets, the results of which show that our proposed GCN model has an average of 59.60% and 57.52% improvement over existing methods on homogeneous datasets and heterogeneous datasets, respectively.

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An approach for reaching consensus in large-scale group decision-making focusing on dimension reduction

Bakhshi,

Ashtiani

2024

Complex Intell. Syst.

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Group decision-making and consensus modeling have always been important research topics. With the widespread use of the Internet, group decisions can be made online, in which a large number of decision-makers participate. Most of the existing studies on large-scale group decision-making consider 20–50 decision-makers. Therefore, there is a need for a framework that focuses on situations where thousands of decision-makers exist. As dimension reduction is one of the five primary challenges in large-scale group decision-making, in this study, after reviewing the existing approaches, a new model is presented using a statistical approach along with complex network analysis techniques. The opinions are generalized first, and then the network of opinions is built. This new method reduces the dimensions of the problem by considering a hierarchy of opinions. Different scenarios were designed for the evaluation. The results show that the effect of this generalization on dimension reduction depends on the parameters of the problem. We have shown that in a group decision scenario with 3000 decision-makers and 6 alternatives, 99% of the data was reduced. As dimension reduction is the main focus of the current research, the effect of consistency on the diversity of opinions has also been investigated, and the results show that opinion consistency affects opinion generalization, which in turn affects dimension reduction. In addition, in the performed simulations, three types of functions were used to calculate similarity. The aim was to determine the best similarity function for the decision problems whose purpose was to rank the available alternatives. The results show that Euclidean similarity is a strict criterion compared with Cosine similarity.

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A signed network analysis-based Consensus Reaching Process in Group Decision Making

Cited by 11 publications

References 42 publications

A trust-enhanced consensus reaching model based on interaction among decision-makers with incomplete preferences

A trust-enhanced consensus reaching model based on interaction among decision-makers with incomplete preferences

A Graph Convolution Neural Network-Based Framework for Communication Network K-Terminal Reliability Estimation

An approach for reaching consensus in large-scale group decision-making focusing on dimension reduction

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