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Trust network analysis has been widely applied in various fields, such as group recommendation, group decision-making and other related areas. In this paper, we focus on obtaining the complete trust network in which experts express their trust relationships for another with a single linguistic term or distribution assessments of a linguistic term set. We first discuss the conditions of obtaining the complete trust network, and the propagation and aggregation of the trust relationships with a single linguistic term. Since the linguistic term set may be symmetric and uniform, symmetric and non-uniform, or asymmetric and non-uniform, we translate linguistic terms into numerical indexes and define the propagation operator based on the semantics of the linguistic term and the Archimedean t-norm. The propagation result is translated to 2−tuple linguistic model because it may not exist in the initial linguistic term set. Some properties are proposed to verify that the proposed operator is compatible with human thought. Then the 2−tuple distribution assessments on a linguistic term set are defined, and the other aggregation operator is proposed to propagate linguistic distribution assessment trust relationships. The second aggregation operator focuses on both the aggregation of linguistic terms and symbolic proportions of linguistic terms and is a generalization of the first operator. Finally, a numerical example of CouchSurfing comparative analyses further demonstrates that the proposed operators are effective and reasonable, and can consider the different semantics of a linguistic term in practical application. [ABSTRACT FROM AUTHOR]

In the context of globalization and information technology advancement, organizations are confronted with the dual challenges of efficiently allocating resources and promptly addressing internal conflicts. The optimization of organizational structures is identified not only as a strategic measure to enhance competitive advantage but also as a necessary approach to improve decision-making quality and organizational adaptability. This study explores the application of artificial intelligence (AI) technologies in optimizing organizational structures, focusing specifically on the intelligent allocation of human resources and the intelligent identification and resolution mechanisms for internal conflicts. Existing research shows a notable deficiency in resource allocation and conflict resolution, particularly lacking consideration of trust network within organizations and analysis of adaptability to dynamic changes. Addressing these issues, a model based on the fuzzy cerebellar model articulation controller (FCMAC) for the optimization of human resource allocation is proposed. This model is capable of dynamically adjusting strategies in response to the evolving demands of the organization. Concurrently, an intelligent framework for identifying and resolving internal conflicts, which incorporates trust network, has been developed. By quantifying trust relationships, the framework aims to enhance the accuracy of decision-making and the coordination within the organization. Findings suggest that these methodologies significantly improve the efficiency of organizational resource allocation and effectively reduce conflict situations, thereby enhancing overall work efficiency and performance. This research not only offers a new perspective on the role of AI in optimizing organizational decisions but also provides practical solutions for management practices, crucial for aiding organizations to adapt to rapidly changing external environments and enhance their competitiveness. [ABSTRACT FROM AUTHOR]

It is necessary to consider the trust relationship among experts in the process of group decision-making, however the trust network and preference information among experts may be incomplete. Therefore, this paper proposes an incomplete probabilistic linguistic multi-attribute group decision-making method based on a three-dimensional trust network. Firstly, the two-dimensional trust network is extended to the three-dimensional form, and the probabilistic linguistic term sets are used to express the trust relationship and degree among experts. On this basis, considering the situation of incomplete information, the trust transfer function is designed to complete the establishment of the trust network. Secondly, in order to complete the incomplete probabilistic linguistic decision preference information of experts, the relative trust of experts and the cosine similarity of preference relations are comprehensively considered. Then, the least average method is used to determine the evaluation information that needs to be adjusted, and different opinion adoption factors are set for a personalized recommendation. Finally, an evaluation case of the national wetland park pilot and comparative analysis are used to demonstrate the effectiveness and applicability of the proposed method. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the attribute weights and decision-makers’ weights were unknown in the emergency decision-making process of public health emergencies, this paper proposed a Pythagorean fuzzy reductive BM operator decision-making method based on DEMATEL and trust network, which took into account the subjective and objective relationships to ensure the integrity of the emergency assessment system. Firstly, considering the important influence of key factors on the emergency decision-making of public health emergencies, it identified and determined attribute weights by the DEMATEL method, and taking the complex and diverse backgrounds of emergency decision-making as the entry point, it used the trust propagation path to improve the trust relationship and built a hybrid trust network that integrated the initial trust degree and preference similarity to obtain decision-makers’ weights. Secondly, considering the strong correlation between the properties of the emergency assessment, it introduced a Pythagorean fuzzy reductive BM operator based on the improved similarity measure and score function, and calculated the comprehensive evaluation value and the ranking of the plan. Finally, this paper applied the method to the emergency management assessment of sporadic cases in hospitals during the normalization of epidemic prevention and control period. It verifies the reliability and reasonable effectiveness of the method. [ABSTRACT FROM AUTHOR]

In this study, a novel top-K ranking recommendation method called collaborative social metric learning (CSML) is proposed, which implements a trust network that provides both user-item and user-user interactions in simple structure. Most existing recommender systems adopting trust networks focus on item ratings, but this does not always guarantee optimal top-K ranking prediction. Conventional direct ranking systems in trust networks are based on sub-optimal correlation approaches that do not consider item-item relations. The proposed CSML algorithm utilizes the metric learning method to directly predict the top-K items in a trust network. A new triplet loss is further proposed, called socio-centric loss, which represents user-user interactions to fully exploit the information contained in a trust network, as an addition to the two commonly used triplet losses in metric learning for recommender systems, which consider user-item and item-item relations. Experimental results demonstrate that the proposed CSML outperformed existing recommender systems for real-world trust network data. [ABSTRACT FROM AUTHOR]

Social recommendation has received great attention recently, which uses social information to alleviate the data sparsity problem and the cold-start problem of recommendation systems. However, the existing social recommendation methods have two deficiencies. First, the binary trust network used by current social recommendation methods cannot reflect the trust level of different users. Second, current social recommendation methods assume that users only consider the same influencial factors when purchasing goods and establishing friendships, which does not match the reality, since users may have different preferences in different scenarios. To address these issues, in this paper, we propose a novel social recommendation framework based on trust and preference, named TPSR, including a trust quantify method based on random walk with restart (TQ_RWR) and a user's primary preference space model (UPPS). Our experimental results in four public real-world datasets show that TQ_RWR can improve the utilization of trust information, and improve the recommended accuracy. In addition, compared with current social recommendation methods/studies, TPSR can achieve a higher performance in different metrics, including root mean square error, precision, recall and F1 value. [ABSTRACT FROM AUTHOR]

The selection of new energy vehicle power battery (NEVPB) recycling service outlet will promote the rapid development of new energy vehicle industry. In order to cope the green and low-carbon development path of NEVPB recycling under information uncertainty and interaction. This paper intends to construct a multidimensional consensus fusion framework of self-confidence Fermatean fuzzy trust network. Firstly, individuals express their opinions based on Fermatean fuzzy sets with self-confidence (FFS-SC). Subsequently, a novel trust propagation and aggregation is built to obtain complete trust relations, and weights of individuals are determined by trust network, self-confidence, and relative deviation. Meanwhile, the trust network- driven consensus feedback mechanism is developed to collect evaluation information. Furthermore, the correlation coefficient to eliminate the homogeneity influence of indicators. In addition, the decision results are derived by Multi-Attributive Border Approximation area Comparison (MABAC) method. Finally, an applied case with five NEVPB recycling service outlets selection is presented to verify the feasibility of the established framework, and the effectiveness is further demonstrated through sensitivity analysis and comparative analysis. The case results show that the indicator with demonstration of recycling service outlet and market supervision departments strengthen supervision indicators have great impact on NEVPB recycling service outlets selection. Furthermore, this paper has reference significance for standardizing the management policy and technology of power battery recycling and realizing the green and low-carbon environmental protection of the whole life cycle of new energy vehicles. [ABSTRACT FROM AUTHOR]

Mobile crowdsensing possesses excellent collaborative capabilities and has become an important solution for addressing complex sensing tasks. However, the high mobility and anonymity of nodes bring security risks such as forging false data and maliciously consuming platform resources. To address these problems, a cooperative detection scheme for malicious nodes based on Dempster-Shafer(D-S) trust evidence reasoning is proposed. Firstly, the sensing platform collects trust data of nodes through a trust evidence chain, which includes data credibility, recommendation trustworthiness, task cooperativeness and behavioral credibility. Meanwhile, trust decay and control mechanisms are adopted to dynamically update the trust evidence chain. Additionally, a recommendation trust network based on recommendation value is constructed through the collaboration between platform and nodes. The Isolation Forest is used to analyze node behavior, and the behavioral credibility is calculated using Bayes' rule. Then, K-Means, Gaussian Mixture Model (GMM), Three-way Decision and Grey Cluster are used to classify nodes into trusted, suspicious and malicious sets. Finally, the purification of suspicious set is achieved through raft consensus among trusted nodes. Simulation tests demonstrate the performance of the proposed scheme in accuracy, false negative rate, false positive rate and time consumption. Overall, the scheme has better detection performance when using GMM. [ABSTRACT FROM AUTHOR]

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As an emerging e-commerce model, livestream e-commerce (LEC) has presented novel opportunities for the advancement of the retail industry. While retailers reap the benefits by this platform, they also encounter challenges associated with platform risks. This paper aims to propose a novel hybrid model to address the risks inherent in livestream e-commerce platforms (LECPs) evaluation problems. Firstly, the paper makes use of q-Rung orthopair fuzzy (q-ROF) trust network (TN) to handle the intricate and uncertain evaluation environment of LECPs and utilizes combinative distance-based assessment (CODAS) to rank the alternative platforms. Secondly, considering that experts have different levels of familiarity with different indicator, a q-ROF-TN matrix between experts is established for each indicator to ascertain their respective weights, which adds robustness to the evaluation process. Thirdly, the dual feedback mechanism is designed to enhance group consensus that attempts to ensure that the evaluation process is collaborative and considers diverse expert opinions, leading to more reliable outcomes. Finally, a set of evaluation indicators is constructed covering various aspects like capital flow management capability and public domain traffic support, and the level of risk of the four LECPs is evaluated using the proposed method. Furthermore, the reliability and effectiveness of the proposed evaluation model are validated through sensitivity analysis and comparative study. [ABSTRACT FROM AUTHOR]

In multi-attribute group decision-making (MAGDM), the social relationships among group members, professional backgrounds and psychological behaviors of decision makers are important factors for achieving high-quality group decisions. However, realizing the consideration of these factors in fuzzy and complex group decision-making scenarios is an important and urgent issue. In MAGDM problems, interval-valued hesitant fuzzy sets (IVHFSs) can better show the hesitant and fuzzy uncertainty information because they are an extension of fuzzy sets. This paper focuses on the score function of IVHFSs and develops a generalized TODIM evaluation method utilizing the novel score functions of IVHFEs and trust networks. The developed method effectively realizes the application of these factors in fuzzy and complex group decision making scenarios. In this contribution, a new deviation function for IVHFSs is created in this paper. It takes into account two types of deviation that are common in IVHFSs: the ambiguity of interval width and the hesitation and uncertainty of multiple intervals. In the sequel, a novel score function for IVHFSs is developed with help of the novel deviation function and the traditional score function. Concerning the perspective of group consistency, a model for determining expert weights is constructed using quality parameters, which combine the novel score function with the trust network. A subjective and objective comprehensive weighting model is made to get attribute weights based on how certain decision information is and the experts' subjective experience. This is done because the more uncertain something is, the higher the risk. Eventually, we develop a generalized TODIM technique for MAGDM based on the novel score function and trust network in IVHFS environment. The developed approach is demonstrated with a preventive engineering decision-making problem. Sensitivity and comparative analyses are implemented to demonstrate the superiority, stability, and validity of the proposed method in this paper. According to the findings of this study, we conclude that the proposed methodology offers a comprehensive and systematic approach to evaluate MAGDM in the context of IVHFS with trust networks. [ABSTRACT FROM AUTHOR]

This study focuses on the consensus-reaching process (CRP) in social networks. Over the years, ample research has been conducted on CRP levels. However, most feedback mechanisms do not consider the decision maker's (DM) willingness to accept the recommendations provided by these mechanisms. Therefore, this study proposes an extensive bounded confidence-based group consensus feedback mechanism that primarily consists of two parts: (a) the construction of a hybrid dynamic trust network and (b) the construction of an extensive bounded confidence model. In the first part, we propose an algorithm to calculate the importance of network nodes and apply it to the trust propagation and aggregation process. In the second part, we propose an extensive bounded confidence model to address the limitations of existing traditional bounded confidence models. In the proposed extensive bounded confidence model, the trust threshold is no longer a "switch" to determine whether a DM interacts with other DMs but a basis to determine the strength of the DMs' interaction; moreover, the hybrid dynamic trust relationship will determine the negotiation sets. A practical case of site selection and comparative analysis verify the effectiveness and flexibility of the proposed mechanism. [ABSTRACT FROM AUTHOR]

Electronic commerce has been growing gradually over the last decade as a new driver of the retail industry. In fact, the growth of e-Commerce has caused a significant rise in the number of choices of products and services offered on the Internet. This is where recommender systems come into play by providing meaningful recommendations to consumers based on their needs and interests effectively. However, recommender systems are still vulnerable to the scenarios of sparse rating data and cold start users and items. To develop an effective e-Commerce recommender system that addresses these limitations, we propose a Trust-Semantic enhanced Multi-Criteria CF (TSeMCCF) approach that exploits the trust relations and multi-criteria ratings of users, and the semantic relations of items within the CF framework to achieve effective results when sufficient rating data are not available. The experimental results have shown that the proposed approach outperforms other benchmark recommendation approaches with regard to recommendation accuracy and coverage.

Negotiation protocols are crucial in automated negotiation since they establish the rules that govern the negotiation process. Multilateral negotiations, i.e., those involving a higher number of participants, are typically more complex than bilateral negotiations and often suffer from issues such as excessive time consumption or a lack of fairness. Some protocols have been developed to address these issues by reducing the complexity of multilateral negotiations: for instance, introducing a mediator to make negotiations more effective. However, many existing approaches overlook the crucial aspect of trust relationships between agents, even though trust significantly influences their actions. To this end, we propose a mediator-based multilateral negotiation protocol that can fully utilize trust networks among negotiating agents and strike a balance between fairness and efficiency. The mediator employs a recommendation mechanism based on a trust network to conduct negotiation, thus leading the negotiation toward the expected outcomes. This recommendation mechanism demonstrates a high efficiency gains under various scenarios where agents use different time-based concede strategies. • A novel trust network embedded negotiation protocol is proposed. • Mediator-based aggregated offers ensure stability and fairness in outcomes. • A new recommendation mechanism enhances negotiation speed in diverse scenarios. [ABSTRACT FROM AUTHOR]

• A hybrid trust network is first formed and dynamically updated with iterations. • A consensus model with a dual-path feedback mechanism is constructed. • DMs can choose whether to conduct modifications while ensuring consensus efficiency. • Trust network-based modifications are recommended while minimizing adjustment cost. This paper proposes a dual-path feedback consensus model based on dynamic hybrid trust relationships to solve multi-attribute group decision-making problems in intuitionistic fuzzy environment. This model comprises two main parts: (a) the construction of a dynamic hybrid trust network among decision makers (DMs) and (b) the formation of a dual-path feedback mechanism to improve the group consensus. In the first part, a hybrid trust network is constructed by combining DMs' prior knowledge of each other and the preference similarities between them. Then, the hybrid trust network is dynamically updated iteratively to reflect the changes in the trust relationships in the process of joint decision-making. In the second part, DMs with low consensus degrees are identified and provided with either a preference or weight adjustment path to improve the group consensus. The preference adjustment path is activated for DMs who agree to modify their preferences, and a nonlinear programming model is proposed to help DMs improve consensus degrees while minimizing adjustment cost. The weight adjustment path is activated for DMs who stick to their own opinions and refuse to make changes, and their weights is adjusted accordingly. An illustrative example along with the related sensitivity analysis and comparative study are used to verify the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Social recommendation has received great attention recently, which uses social information to alleviate the data sparsity problem and the cold-start problem of recommendation systems. However, the existing social recommendation methods have two deficiencies. First, the binary trust network used by current social recommendation methods cannot reflect the trust level of different users. Second, current social recommendation methods assume that users only consider the same influencial factors when purchasing goods and establishing friendships, which does not match the reality, since users may have different preferences in different scenarios. To address these issues, in this paper, we propose a novel social recommendation framework based on trust and preference, named TPSR, including a trust quantify method based on random walk with restart (TQ_RWR) and a user’s primary preference space model (UPPS). Our experimental results in four public real-world datasets show that TQ_RWR can improve the utilization of trust information, and improve the recommended accuracy. In addition, compared with current social recommendation methods/studies, TPSR can achieve a higher performance in different metrics, including root mean square error, precision, recall and F1 value.

This article explores the foundations of trust between Czechoslovak state bodies and experts selected for foreign service in Africa. The focus is on the means through which this trust was challenged during long periods of separation from socialist ways of life, ways which were reinvented after the systemic political changeover in Czechoslovak administration after August 1968. Drawing on the concept of "navigation", it examines the strategies experts developed to earn and restore credibility in the eyes of party authorities after the total disintegration of prior networks of trust following the Warsaw Pact invasion of Czechoslovakia. During their tenure abroad, experts established trust networks on various levels - not only with Czechoslovak political representatives, but also with other experts in common agencies and even officers of the Czechoslovak Intelligence Service. These personal ties proved to be instrumental for negotiating future career prospects after the stormy years of 1968-1969. However, contrary to popular belief, this article demonstrates that it was less political attitude and more the economic potential of the experts' international positions that determined their ongoing credibility as "honoured" citizens. [ABSTRACT FROM AUTHOR]

Social network analysis offers an understanding of our modern world, and it affords the ability to represent, analyze and even simulate complex structures. While an unweighted model can be used for online communities, trust or friendship networks should be analyzed with weighted models. To analyze social networks, it is essential to produce realistic social models. However, there are serious differences between social network models and real-life data in terms of their fundamental statistical parameters. In this paper, a genetic algorithm (GA)-based social network improvement method is proposed to produce social networks more similar to real-life data sets. First, it creates a social model based on existing studies in the literature, and then it improves the model with the proposed GA-based approach based on the similarity of the average degree, the k-nearest neighbor, the clustering coefficient, degree distribution and link overlap. This study can be used to model the structural and statistical properties of large-scale societies more realistically. The performance results show that our approach can reduce the dissimilarity between the created social networks and the real-life data sets in terms of their primary statistical properties. It has been shown that the proposed GA-based approach can be used effectively not only in unweighted networks but also in weighted networks. [ABSTRACT FROM AUTHOR]

Trust-aware recommendation approaches are widely used to mitigate the cold-start problem in recommender systems by utilizing trust networks. In this paper, we point out the problems of existing trust-aware recommendation approaches as follows: (P1) exploiting sparse explicit trust and distrust relationships; (P2) considering a misleading assumption that a user pair having a trust/distrust relationship certainly has a similar/dissimilar preference in practice; (P3) employing the transitivity of distrust relationships. Then, we propose TrustRec, a novel approach based on the matrix factorization that provides an effective solution to each of the afore-mentioned problems and incorporates all of them in a single matrix factorization model. Furthermore, TrustRec exploits only top-k most similar trustees and dissimilar distrustees of each user to improve both the computational cost and accuracy. The results of our extensive experiments demonstrate that TructRec outperforms existing approaches in terms of both effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

The recommendation system is an important method to deal with the problem of information overload. The existing trust network-based recommendation algorithm does not fully mine the user trust relationship information and affects the recommendation effect. This paper proposed a model called CETrust, which took into account the direct trust and indirect trust among users and integrated the attribute information of the recommended project into the factorization of probability matrix. Experiments show that the recommendation accuracy of the newly proposed recommendation algorithm (H-CETrust) is higher than that of the current recommendation algorithms. [ABSTRACT FROM AUTHOR]

Due to the urgent nature of emergency decision making, it is necessary to reach the consensus requirement quickly. Ordinal consensus measure explores the relation between the rankings and helps to intuitively know which alternative needs to be adjusted to accelerate the improvement of consensus. Moreover, decision makers (DMs) in the decision making problem are often connected through trust relationships which affect the DMs' judgments in the process of DMs' interaction. Therefore, this paper explores trust network-based group decision-making in which the consensus level is estimated by an ordinal consensus measure. We first focus on the supplementation of an incomplete trust network. One of the most common methods is to design the trust propagation operator, whereas the intensity of information propagation may be different in various scenarios. Therefore, considering the different numerical scale of the linguistic term set, a trust propagation operator with different intensity of trust propagation is designed to obtain the indirect trust relationship. In the process of supplementing the incomplete trust network, the contribution of DMs to propagating information is concerned, which can be described by the betweenness centrality, and the importance weights of DMs are determined by combining the betweenness centrality and trust in-degree. In the consensus reaching process, we first propose an improved ordinal consensus measure, which takes into account the consistency of orders of the same alternative in different rankings as well as the importance of positions of alternatives. Then, we design the identification rule and the feedback mechanism for those with low consensus levels. The identification rule is used to select the DMs which first few alternatives in the ranking are different with those in the ranking of group. And in the feedback mechanism, the referenced preference relation (FPR) obtained by the trust network is provided for the identified DMs. Afterwards, combining the referenced FPR, an optimization model is designed to give the adjustment opinion. Finally, a numerical example elaborates on the feasibility of the trust propagation operator and consensus model. The comparative analysis demonstrates the rationality and effectiveness of the proposed model. • A trust propagation operator with the intensity of trust propagation. • Considering the trust in-degree and betweenness centrality in DM's weights. • An ordinal consensus considered consistency of order and importance of position. • Considering the referenced information in the feedback mechanism. [ABSTRACT FROM AUTHOR]

Recommender algorithms are usually modeled based on user behavior data.However, the sparseness of explicit behavior data may cause the cold start problem of recommender algorithms.In order to solve the impact of data sparseness and cold-start problems on the effect of recommender algorithms, implicit trust relationship based on user similarity was introduced based on the existing revealed trust relationship, and a new recommender algorithm was designed through the SVD++ implicit semantic model.In order to improve the effect of the algorithm, the neighborhood model was integrated further, and the algorithm score prediction formula and loss function were derived.In the Epinions open source data set, RMSE and MAE were used as test indicators, and comparative experiments were conducted on the entire user set and the cold start user set.The experimental results show that the recommender algorithm can optimize the cold start problem of the original recommender algorithm to a certain extent, and achieve a better rating prediction accuracy.

A nation’s progress and development depends on the production of knowledge by its educational centers and turning them into presentable products by the industrial units. To reach this point, a coherent connection between the scientific and industrial units in the country seems natural and inevitable. This research aims at scrutinizing the cooperation network between Hormozgan University and Industry. This study has an applied and practical nature and can be classified as a descriptive and survey-based research. The statistical population for the study includes Hormozgan University faculty members and managers of research centers in a number of Hormozgan organizations. In order to analyze this cooperative network, at first, two sets of questionnaires containing items for studying the network connections were designed. After completing the questionnaires, the data for each network was imported into Excel as a matrix and analyzed by using Visone and NetDraw applications and indexes such as the inner core angle, cutpoints and network densities were derived and computed for the networks. All in all, the cooperation and trust networks analysis indicated that the presented networks had a relatively low central density. The high points of isolation in two networks and the lower density of networks of trust compared to the cooperative networks indicate that the two parties have not been able to develop and establish a good level of satisfaction and trust in the intended cooperative paths and, consequently, it can be maintained that there is an almost weak level of communication and trust between Hormozgan University and business environments (i.e., industry) in these networks.