Trust Network Research Articles

The role of social capital in enhancing the welfare of pre-prosperous families

This study delves into the intricate relationship between social capital and the economic resilience of pre-prosperous families in South Sulawesi Province, Indonesia, focusing mainly on the elements of trust, reciprocal relationships, and social networks. A quantitative research methodology was employed, and a sample of 217 impoverished families was randomly selected from a total population of 103,015 households spanning nine regencies and cities within the province. Through the application of structural equation modeling, the research demonstrates the substantial impact of social capital on both household expenditure and the overall welfare of impoverished families, highlighting its fundamental role in bolstering economic well-being within these communities. The study's findings underscore the critical importance of social capital in not only augmenting household spending but also in enhancing the welfare of economically disadvantaged families, with broader implications for combating poverty, reducing socioeconomic disparities, and fostering sustainable economic progress throughout the region. By elucidating the ways in which social capital shapes the microeconomic resilience of families, this research highlights the transformative potential of social capital in driving economic development and improving the quality of life for less affluent households in South Sulawesi Province. Policymakers and practitioners can utilize these insights to develop targeted interventions that empower local residents, foster supportive networks, and ultimately bolster the economic resilience of disadvantaged families, contributing to sustainable development and poverty alleviation efforts.

Peers and value preferences among adolescents in school classes: a social network and longitudinal approach

The aim of our study was twofold: (1) to explore the role of value preferences on peer relations in school classes (selection effect) and (2) to explore the role of peers’ values on adolescents’ values (influence or socialization effect) in three types of networks (friendship, advice, and trust). To answer these questions, we used a longitudinal social network approach in a study of N = 903 adolescents (57% girls) from 34 secondary school classes in Poland. Pupils began participating in the study when they joined their secondary school and were followed over two and a half years. Panel data were collected at six measurement time points during this period. Values were conceptualized according to the values theory proposed by Schwartz and measured by the Portrait Value Questionnaire. The collection of network data followed a roster design. Pupils were asked to evaluate the strength of their friendships, as well as the frequency with which they approached peers to ask for advice about school or homework or to talk about things that are important to them in the last 2 weeks. We found empirical support for both selection and socialization effects, especially for protection values (Conservation and Self-enhancement). The selection effect was most evident in advice and trust networks and the socialization effect was particularly prevalent in friendship and trust networks.

Dynamic trust network-driven consensus modeling with endogenous adjustment and exogenous modification under a quasi-Z-number environment

The Z-number is widely used in decision-making because of its unique dual ability to capture human knowledge and facilitate judgments on realistic issues. To improve the applicability of Z-numbers, this paper puts forward the concept of quasi-Z-number and studies the social network and consensus models with quasi-Z-information to address multi-attribute group decision-making problems. First, a dynamic trust network among decision makers (DMs) is constructed. The endogenous adjustment mechanism is presented due to the interaction among DMs. Second, a feedback mechanism for non-consensus opinions is established. In the feedback, an exogenous modification mechanism is constructed through the minimal modification consensus model to help non-consensus DMs improve their consensus level. Furthermore, a minimum tolerance increment consensus model is proposed to cope with the situation where the consensus threshold is not reached within a specified number of iterations. Third, the practicability of the proposed decision-making system is illustrated by the case of a green energy investment in which all technicalities are shown. Its comprehensive performance is exhibited in the sensitivity analysis. The validity of the information form and methodology is demonstrated in the comparative analysis.

Network Perceptions and Trust: How the Ties in Our Mind Influence Perceived Trustworthiness

Network Perceptions and Trust: How the Ties in Our Mind Influence Perceived Trustworthiness

Overlapping community-driven dynamic consensus reaching model of large-scale group decision making in social network

Large-scale group decision-making (LSGDM) serves as a pivotal tool for facilitating consistent decision results through intricate interactions among individuals within the network. However, the impact of complex trust relationships within overlapping communities on consensus is often overlooked in many studies. Moreover, the dynamic interaction between overlapping communities and the consensus reaching process (CRP) is seldom taken into account. This paper aims to build an overlapping community-driven consensus reaching model for addressing LSGDM challenges in social network. Given the advantages of intuitionistic fuzzy numbers (IFNs) in uncertain information representation, IFNs are used to express evaluation information and trust information. Firstly, a novel overlapping community detection method is developed to divide subgroups and detect overlapping communities. Secondly, to determine reliable subgroup weights, this paper constructs a weight determination model that considers multiple factors and their internal correlations. Then, an overlapping community-driven dynamic consensus model is proposed, which provides a new way to resolve the opinion conflicts, considering the dynamic change of CRP. Simultaneously, the reverse effect of opinion adjustment on the social trust network is considered. Finally, the practicality of the proposed model is demonstrated through illustrative cases. Furthermore, through a comparative analysis, the superiority of the proposed model is demonstrated and the efficiency improvement for CRP is verified.

The Role of Knowledge Brokers in Facilitating Knowledge Transfer within and between Organizations

Purpose: The general objective of the study was to investigate the role of knowledge brokers in facilitating knowledge transfer within and between organizations. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the role of knowledge brokers in facilitating knowledge transfer within and between organizations. Preliminary empirical review revealed that knowledge brokers played a crucial role in facilitating knowledge transfer within and between organizations by acting as intermediaries between knowledge creators and users. They effectively bridged gaps, built networks of trust, translated complex information into accessible formats, and provided tailored support to various stakeholders. Their efforts enhanced the accessibility, relevance, and application of knowledge, fostering a culture of continuous learning and innovation. Additionally, knowledge brokers contributed to the broader diffusion of innovations and best practices, promoting systemic improvements and a more interconnected knowledge ecosystem. Unique Contribution to Theory, Practice and Policy: The study recommended enhancing theoretical frameworks to explicitly include knowledge brokers, formally recognizing and supporting their roles within organizations through training and development, and creating policies that incentivize their integration into public and private sectors. It also suggested developing interdisciplinary networks for collaboration, implementing robust evaluation mechanisms to assess the impact of knowledge brokers, and ensuring long-term sustainability through ongoing funding and institutional support. These measures aimed to optimize the impact of knowledge brokers in driving innovation, improving performance, and facilitating effective knowledge transfer within and between organizations.

A cooperative detection scheme for malicious nodes based on D-S trust evidence reasoning in mobile crowdsensing networks

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.

Developing Farmers’ Community Resilience in the Volcanic Area of Mount Merapi, Indonesia

Mount Merapi is one of the active stratovolcanoes in Indonesia, and its slopes are not only home to the powerful forces of nature but also to thriving communities of farmers. Many farmers' communities are found around the slopes of this mountain, relying on the fertile volcanic soil for their agricultural activities. This research was undertaken near Mount Merapi, approximately 4 km from the volcano peak, to investigate the resilience of farmers' communities in the face of Mount Merapi's potential disasters. It affects the farmer’s behavior, with the majority of residents being farmers. The primary objective of this study was to understand why and how these farmers’ communities demonstrate resilience in the wake of Mount Merapi's potential disasters. The qualitative approach was used in this research and the key informants were determined by using the purposive sampling technique. Data obtained were analyzed using an interactive data analysis model. The study identified three primary themes that significantly influenced the resilience of farmers' communities. The first theme encompasses social aspects, highlighting the importance of social capital, including strong social relations, trust, and community networks. The second theme revolves around economic factors, specifically business capital and the various forms of income within the farmers' communities. Lastly, the third theme focuses on environmental considerations, emphasizing the role of natural capital, particularly effective land use, as the most impactful in maximizing community resilience. Additionally, it was also found that the strong engagement among farmers within their communities made their resilience ability toward disaster highly formidable

The Role of Social Capital in the Implementation of Public Health Programs in West Kendari District, Kendari City

This study aims to find out and describe the forms and roles of social capital in the community of West Kendari District in supporting the implementation of public health programs. This study uses a qualitative approach with a descriptive model, the research informants totaled 12 people who were selected by purposive sampling technique. The data in this study was obtained through interviews, observations and document studies, the data obtained was then analyzed qualitatively with an interactive model consisting of data reduction, data presentation and conclusion drawn. The results of the study show that based on the results and discussion above, the conclusion that can be drawn is that social capital in West Kendari District plays an important role in supporting the implementation of public health programs through three main forms: trust, norms, and social networks. Trust between citizens strengthens cooperation and active participation in various health programs, creating an environment that supports efforts to improve public health. Existing social norms facilitate collective action, strengthen social cohesion, and ensure that health efforts are carried out together with a strong commitment from all elements of society. In addition, social networks serve as an important tool for disseminating health-related information and rallying support from various parties, thereby expanding the reach of health programs and ensuring their sustainability. Thus, strengthening trust, norms, and social networks in West Kendari District is the key to increasing the effectiveness and sustainability of public health programs implemented in the region.

A generalized TODIM evaluation approach based on the novel score function and trust network under interval-valued hesitant fuzzy environment

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.

Consensus reaching in LSGDM: Overlapping community detection and bounded confidence-driven feedback mechanism

The surge of social media has made large-scale group decision-making (LSGDM) crucial in real-world decision-making. The intricacies of trust relationships within social networks that emerged from relations in social media affect both the clustering and the consensus of large groups. However, existing research often neglects the impact of overlapping social trust networks on group consensus. To fill this gap, this study introduces a novel consensus-reaching process (CRP) that integrates overlapping community detection and ELICIT-based optimization models under bounded confidence. Initially, the Lancichinetti-Fortunato method (LFM) is employed to identify overlapping community structures within social trust networks, delineating several subgroups and identifying corresponding non-overlapping and overlapping decision-makers (DMs). Subsequently, the PageRank (PR) algorithm is utilized to compute both global and local weights for individuals, facilitating a rational aggregation of collective and subgroup opinions. Next, two-stage Extended Comparative Linguistic Expressions With Symbolic Translation (ELICIT)-based optimization consensus models under bounded confidence are designed, aiming to provide optimal feedback for guiding DMs' preference adjustments. Since overlapping DMs may belong to multiple subgroups, a weighted influence feedback mechanism is introduced to mitigate conflicting guidance from these multiple affiliations. Finally, we demonstrate the effectiveness and superiority of our proposed method through numerical validation and comparative analysis against existing approaches.

Building trust in cybernetic payment network: Insights from an emerging economy

This study investigates customers' trust and behavioural intentions regarding the use of cybernetic payment network services in an emerging country, focusing on perceived quality, system quality, and perceived image. Data from 324 respondents in Bangladesh were collected via a self-administered Google survey form distributed through email, WhatsApp, and Messenger using purposive sampling. The SmartPLS-4 software was used to assess construct validity and hypothesized path coefficients. Results indicate that perceived quality, system quality, and image significantly influence perceived trust in cybernetic payment network services. Additionally, perceived trust and system quality positively impact behavioural intention to use these services. Furthermore, perceived trust mediates the relationship between behavioural intention and system quality, perceived quality, and image. The study underscores the importance for cybernetic payment network service providers to enhance perceived quality, system quality, and perceived image to foster trust and promote adoption. It highlights the pivotal role of trust in driving customers' intention to adopt these services, urging providers to prioritize efforts in building trust among users. This study contributes to the limited literature on the impact of perceived quality, system quality, and perceived image on customers' inclination to use cybernetic payment network services, offering valuable insights for bank managers and policymakers to formulate effective strategies for sustaining innovation and enhancing adoption rates.

Interactive dynamic trust network for consensus reaching in social network analysis based large-scale decision making

With the advances in social media and e-democracy technologies, large-scale decision making (LSDM) problems with trust relationships demands effective consensus-reaching processes. Existing literature has identified a need for improvement in the updating method of trust relationships and the recommendation-feedback mechanism, while the overall effect of trust relationships on consensus reaching remains unexplored and not quantitatively examined. This study proposes a novel interactive dynamic trust-lead consensus reaching process (IDT-CRP) based model to address these challenges. The model integrates a dynamic Trust network updating (DTNU) process and a trust-lead and efficiency-driven recommendation-feedback (TERF) process. In the DTNU process, secondary trust is defined to represent the trust derived from decision makers’ modification behaviors in each iteration. In the TERF process, a novel trust-lead and efficiency-driven recommendation-feedback mechanism is proposed, in which two different decision scenarios are provided. Furthermore, the role of trust relationships in enhancing group consensus level is analyzed, revealing its significant impact while identifying an upper limit. Novelties of this paper include 1) the improved trust updating algorithm which derived secondary trust from modification behaviors instead of opinion similarities; 2) the improved recommendation-feedback mechanism with different trust utilization scenarios while retaining minority opinions; 3) the novel IDT-CRP model based on the interactive mechanism between trust relationship and opinion evolution. A series of experiments are implemented, validating that the proposed model is of good feasibility, effectiveness and robustness, and can be applied in future LSDM research.

Analysis of accidents in maritime transport using the method of Bayesian trust networks

Maritime incidents, though rare, have a significant impact on both the global economy and the environment. Improving maritime navigation practices always requires new ways to enhance safety, inevitably involving learning from past experiences and mistakes. In this context, probabilistic analysis of incidents and their associated consequences can play a crucial role in creating a safer and more efficient maritime transportation system. Bayesian networks constitute a class of probabilistic models based on statistics, decision theory, and graph theory. This paper describes the analysis of maritime incident statistics by selecting probabilistic parameters influencing the risk of their occurrence. Important parameters from this database are grouped, and a Bayesian network is constructed to illustrate the relationships between them. This, in turn, provides insight into the dependencies existing among the variables in the database and the fundamental reasons for these accidents. The data for this study are based on the Lloyds Register and IMO incident databases from 1990 to 2022. Key factors from this database are grouped, and a Bayesian network is built to show the relationships between the corresponding variables, providing an understanding of the probabilistic dependencies among the variables in the database and the primary causes of these incidents.

Consensus reaching with the dynamic hybrid trust network in group decision making based on an extensive bounded confidence

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.

TPoison: Data-Poisoning Attack against GNN-Based Social Trust Model

In online social networks, users can vote on different trust levels for each other to indicate how much they trust their friends. Researchers have improved their ability to predict social trust relationships through a variety of methods, one of which is the graph neural network (GNN) method, but they have also brought the vulnerability of the GNN method into the social trust network model. We propose a data-poisoning attack method for GNN-based social trust models based on the characteristics of social trust networks. We used a two-sample test for power-law distributions of discrete data to avoid changes in the dataset being detected and used an enhanced surrogate model to generate poisoned samples. We further tested the effectiveness of our approach on three real-world datasets and compared it with two other methods. The experimental results using three datasets show that our method can effectively avoid detection. We also used three metrics to illustrate the effectiveness of our attack, and the experimental results show that our attack stayed ahead of the other two methods in all three datasets. In terms of one of our metrics, our attack method decreased the accuracies of the attacked models by 12.6%, 22.8%, and 13.8%.

Privacy-Preserving and Diversity-Aware Trust-based Team Formation in Online Social Networks

As online social networks (OSNs) become more prevalent, a new paradigm for problem-solving through crowd-sourcing has emerged. By leveraging the OSN platforms, users can post a problem to be solved and then form a team to collaborate and solve the problem. A common concern in OSNs is how to form effective collaborative teams, as various tasks are completed through online collaborative networks. A team’s diversity in expertise has received high attention to producing high team performance in developing team formation (TF) algorithms. However, the effect of team diversity on performance under different types of tasks has not been extensively studied. Another important issue is how to balance the need to preserve individuals’ privacy with the need to maximize performance through active collaboration, as these two goals may conflict with each other. This research has not been actively studied in the literature. In this work, we develop a team formation (TF) algorithm in the context of OSNs that can maximize team performance and preserve team members’ privacy under different types of tasks. Our proposed PR iv A cy- D iversity- A ware T eam F ormation framework, called PRADA-TF , is based on trust relationships between users in OSNs where trust is measured based on a user’s expertise and privacy preference levels. The PRADA-TF algorithm considers the team members’ domain expertise, privacy preferences, and the team’s expertise diversity in the process of team formation. Our approach employs game-theoretic principles Mechanism Design to motivate self-interested individuals within a team formation context, positioning the mechanism designer as the pivotal team leader responsible for assembling the team. We use two real-world datasets (i.e., Netscience and IMDb) to generate different semi-synthetic datasets for constructing trust networks using a belief model (i.e., Subjective Logic) and identifying trustworthy users as candidate team members. We evaluate the effectiveness of our proposed PRADA-TF scheme in four variants against three baseline methods in the literature. Our analysis focuses on three performance metrics for studying OSNs: social welfare, privacy loss, and team diversity.

CTITF: A tensor factorization model with constrained bidirectional user trust and implicit feedback for context-aware recommender systems

Recommender systems offer an efficient solution to the problem of information overload, which is exacerbated by the rapid expansion of data. Context-aware recommender systems (CARS) have become prominent, incorporating contextual information to provide more desirable recommendations. While the dimension of contextual information rises, the complexity of algorithms increases as well, highlighting the data sparsity problem. In this paper, we propose CTITF, a novel tensor factorization model that incorporates constrained user bidirectional trust and implicit feedback. First, CTITF distinguishes the roles of trust networks in terms of the directionality of trust relationships: the trustor and the trustee. CTITF then constructs trust influences and biases for both roles to linearly represent user preferences. Furthermore, CTITF improves the rating prediction function and recommendation objective function based on CP factorization. Finally, CTITF filters out trust relationships where users do not have the same history items. It also uses contextual information to limit the amount of implicit feedback, addressing the issue of high time complexity. Experiments on two real-world datasets illustrate that the proposed model outperforms previous TF-based models and social recommender systems in terms of rating prediction accuracy. In addition, the CTITF model reduces the training time by 50.28% compared to the trustTF model, which also uses trust.

Self-confidence Fermatean fuzzy trust network-driven consensus fusion framework: A new energy vehicle power battery recycling service outlet selection

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.

Collaboration Governance On Ecotourism Belitung Unesco Global Geopark

This study aims to analyze the governance of collaboration of the Belitung Ecotourism UNESCO Global Geopark. Attention is given to the study of collaboration governance considered one of the key issues of success in the context of Belitung tourism development as part of the UNESCO Global Geopark network. The contribution and role of each actor whether from the government, private sector, community, or community has made a positive contribution to building sustainable and environmentally sound tourism quality. This research uses a sequential mixed methods research exploratory approach, in this research data is obtained through in-depth interviews, document reviews, and surveys so that the triangulation process can be carried out more completely. Research informants consisted of elements of government, the private sector, associations, society, and communities. A total of 13 informants were interviewed and 31 people responded to the survey. The results show that trust, values, and social networks have become the glue and bind each actor so that they function as a basic element to form a good collaboration.