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The 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.

Firstly, 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.

The 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.

This 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.

This study aims to construct a Weber point-based model to complete the visualization of preference aggregation in group decision-making problem, in which decision-makers are associated with trust relationship.

This study mainly comprises four parts: trust propagation, preference aggregation, opinion adjustment and alternative selection. Firstly, the incomplete trust between decision-makers is completed with trust transfer operators and propagation probability in trust propagation process. Secondly, a preference aggregation model based on Weber point is proposed to aggregate the group preference visually. Thirdly, opinions are adjusted to reach a consensus. Finally, the ranking of alternatives is determined by the correlation coefficient with the group preference as a reference.

The Weber point-based model proposed in this study can minimize the gap in the preference of alternatives between the group and all decision-makers, and realize the visualization of aggregation result. A case of plan selection is introduced to illustrate the feasibility and effectiveness of the proposed model.

By comparing the result with the weighted average-based preference aggregation method, the Weber point-based model proposed in this study can show the result of preference aggregation intuitively and improve group consensus.

Green technology adoption (GTA) in small and micro enterprises (SMEs) is a complex multi-attribute group decision-making issue. Conflicts of opinions can hamper the achievement of group coherence. The purpose of this paper is to solve the conflict decision-making problem in SMEs.

This paper reconstructs the GTA evaluation index system for SMEs and proposes an intragroup coordination relationship model to mitigate group decision-making conflicts based on the cloud model and social networks (GCS). The numerical characteristics of these GTA indicators are determined using fuzzy theory and cloud model.

It was found that establishing a trust relationship network and a conflict coordination relationship could improve the consistency of group decision-making. The degree of coordination has subtle differences with the trust relationship. Particularly, the coordination relationship has obvious influences on the decision-making results among small groups in SMEs.

The conflict coordination model can significantly improve the consistency of group decision-making in SMEs. At the same time, it discusses the differences between the level of conflict coordination and the trust propagation in the process of group decision-making.

This study aims to propose a consensus model that considers dynamic trust and the hesitation degree of the expert's evaluation, and the model can provide personalized adjustment advice to inconsistent experts.

The trust degree between experts will be affected by the decision-making environment or the behavior of other experts. Therefore, based on the psychological “similarity-attraction paradigm”, an adjustment method for the trust degree between experts is proposed. In addition, we proposed a method to measure the hesitation degree of the expert's evaluation under the multi-granular probabilistic linguistic environment. Based on the hesitation degree of evaluation and trust degree, a method for determining the importance degree of experts is proposed. In the feedback mechanism, we presented a personalized adjustment mechanism that can provide the personalized adjustment advice for inconsistent experts. The personalized adjustment advice is accepted readily by inconsistent experts and ensures that the collective consensus degree will increase after the adjustment.

The results show that the consensus model in this paper can solve the social network group decision-making problem, in which the trust degree among experts is dynamic changing. An illustrative example demonstrates the feasibility of the proposed model in this paper. Simulation experiments have confirmed the effectiveness of the model in promoting consensus.

The authors presented a novel dynamic trust consensus model based on the expert's hesitation degree and a personalized adjustment mechanism under the multi-granular probabilistic linguistic environment. The model can solve a variety of social network group decision-making problems.

This paper aims to put forward a kind of new method of intelligent trust engineering metrics for application of mobile ad hoc network (MANET).

The new method calculates the communication trust by using the number of data packets among the nodes of MANET, predicts intelligently the trust and calculates the comprehensive trust based on the historical trust; then calculates the energy trust based on the residual energy of the nodes of MANET, calculates the direct trust based on the communication trust and energy trust. The new method calculates the recommendation trust based on the recommendation reliability; adopts the adaptive weighting to calculate the integrated direct trust by considering the direct trust with recommendation trust.

Based on the integrated direct trust and the factor of trust propagation distance, the indirect trust among the nodes of MANET is calculated. The above process can be optimized based on the dynamic machine learning presented in this study. The advantage of the new method is its intelligent ability to discover malicious nodes.

The advantage of the new method is its intelligent ability to discover malicious nodes which can partition the network by falsely reporting other nodes as misbehaving and proceeds to protect the network. The authors have done the experiments based on the tool kits such as NS3, QualNet, OMNET++ and MATLAB. The experimental results show that this study’s approach can effectively avoid the attacks of malicious nodes, and more conformable to the actual engineering application of MANET.

This paper aims to improve the recommendations performance for cold-start users and controversial items. Collaborative filtering (CF) generates recommendations on the basis of similarity between users. It uses the opinions of similar users to generate the recommendation for an active user. As a similarity model or a neighbor selection function is the key element for effectiveness of CF, many variations of CF are proposed. However, these methods are not very effective, especially for users who provide few ratings (i.e. cold-start users).

A new user similarity model is proposed that focuses on improving recommendations performance for cold-start users and controversial items. To show the validity of the authors’ similarity model, they conducted some experiments and showed the effectiveness of this model in calculating similarity values between users even when only few ratings are available. In addition, the authors applied their user similarity model to a recommender system and analyzed its results.

Experiments on two real-world data sets are implemented and compared with some other CF techniques. The results show that the authors’ approach outperforms previous CF techniques in coverage metric while preserves accuracy for cold-start users and controversial items.

In the proposed approach, the conditions in which CF is unable to generate accurate recommendations are addressed. These conditions affect CF performance adversely, especially in the cold-start users’ condition. The authors show that their similarity model overcomes CF weaknesses effectively and improve its performance even in the cold users’ condition.

The challenge of pervasive computing consists in offering access to computing services anywhere and anytime with any devices. However, before it becomes a reality, the problems of access control and authentication have to be solved, among others. Existing solutions are inadequate without adaptation to this specific environment. Among the promising approaches, the trust paradigm seems to be more flexible than others. We base this proposal on this paradigm to implement a distrust model, so‐called APC (Access Pass Certificate). The main objective of this model is to enable authorized user to roam and to access trusted sites though they are not known locally. A user can claim two kinds of APCs provided by two kinds of sites: the home site (where the user has an account) and the trusted site (that trusts the user). Using these certificates, the user can progressively extend her access scope. This model implements a decentralized mapping policy, where the correspondence between the user’s home profile and her rights in the trusted sites is determined by the trusted site. This distrust model and its implementation are presented in this article where we exhibit its importance for large but controlled access in pervasive environments.

Mobile ad hoc networks rely on cooperation to perform essential network mechanisms such as routing. Therefore, network performance depends to a great extent on giving participating nodes an incentive for cooperation. The level of trust among nodes is the most frequently used parameter for promoting cooperation in distributed systems. There are different models for representing trust, each of which is suited to a particular context and leads to different procedures for computing and propagating trust. The goal of this study is to analyze the most representative approaches for mobile ad hoc networks. It aims to obtain a qualitative comparison of the modeling approaches, according to the three basic components of a trust model: information gathering, information scoring and ranking, and action execution.

The paper identifies the different tasks required by a trust system and compares the way they are implemented when the system model itself is based on information theory, social networks, cluster concept, graph theory and game theory. It also provides a common nomenclature for the models. The study concentrates exclusively on the trust models themselves, without taking into account other aspects of the original articles that are beyond the scope of this analysis.

The study identifies the main components that a trust model must provide, and compares the way they are implemented. It finds that the lack of unity in the different proposed approaches makes it difficult to conduct an objective comparison. Finally, it also notices that most of the models do not properly manage node reintegration.

The best of our knowledge, the study is the first that uses information scoring and ranking as classification key. According to this key, approaches can be classified as based on information theory, clusters and social network theory, and cooperative and non‐cooperative game theory. It also provides a common nomenclature for all of them. Finally, the main contribution of the paper is to provide an analysis of the most representative approaches and present a novel qualitative comparison.

This paper aims to propose a trust-based ant-colony recommender system. It achieves high accuracy and coverage by integrating the importance level of friends. This paper has two main contributions, namely, selecting higher-quality raters and improving the prediction step. From these two contributions, the proposed trust-based ant-colony recommender system could provide more accurate and wider-coverage prediction than existing systems.

To obtain higher-quality raters, the data set was preprocessed, and then, trust values were calculated. The depth of search was increased to obtain higher coverage levels. This work also focuses on the importance level of friends in the system. Because the levels of influence on the active user of all friends are not equal, the importance level of friends is integrated into the system by transposing rater’s rating to the active user’s perspective and then assigning a weight to each rater.

The experimental evaluation clearly demonstrates that the proposed method achieves better results in terms of both accuracy and coverage than existing trust-based recommender systems. It was found that integrating the importance level of friends into the system, which transposes ratings and assigns weight to each user, can increase accuracy and coverage.

Existing trust-based ant-colony recommender systems do not consider the importance level of friends in the prediction step. Most of them only focus on finding raters and then using the rater’s real ratings in the prediction step. A new method is proposed that integrates the importance level of friends into the system by transposing a rater’s rating to match the active user’s perspective and assigning a weight for each rater. The experimental evaluation demonstrates that the proposed method achieves better accuracy and coverage than existing systems.

This paper aims to address some security issues in open systems such as service‐oriented applications and grid computing. It proposes a security framework for these systems taking a trust viewpoint. The objective is to equip the entities in these systems with mechanisms allowing them to decide about trusting or not each other before starting transactions.

In this paper, the entities of open systems (web services, virtual organizations, etc.) are designed as software autonomous agents equipped with advanced communication and reasoning capabilities. Agents interact with one another by communicating using public dialogue game‐based protocols and strategies on how to use these protocols. These strategies are private to individual agents, and are defined in terms of dialogue games with conditions. Agents use their reasoning capabilities to evaluate these conditions and deploy their strategies. Agents compute the trust they have in other agents, represented as a subjective quantitative value, using direct and indirect interaction histories with these other agents and the notion of social networks.

The paper finds that trust is subject to many parameters such as the number of interactions between agents, the size of the social network, and the timely relevance of information. Combining these parameters provides a comprehensive trust model. The proposed framework is proved to be computationally efficient and simulations show that it can be used to detect malicious entities.

The paper proposes different protocols and strategies for trust computation and different parameters to consider when computing this trust. It proposes an efficient algorithm for this computation and a prototype simulating it.