Reputation Propagation Research Articles

A Corporate Reputation Propagation Dynamic Model

This study analyzes the process of corporate reputation (CR) propagation. We consider that different positions play different roles via social word of mouth (sWOM). In essence, our interest lies in the following question: how does sWOM affect the process of CR propagation? As a benchmark, we develop a dynamic model to reflect the factors and laws influencing corporate reputation propagation. We find that the stability of the system is not what corporations want to see in the propagation of positive CR after analyzing two basic reproduction numbers and conducting a numerical simulation. Another important finding is that stability is what corporations expect in the propagation of negative CR. This study makes the logical analysis of the relationship between CR and consumer loyalty more complete and helps us to clearly understand the overall process of CR propagation. The study provides a new way to analyze the law of CR propagation and can provide a reference for decision-making for corporations.

A Hybrid Social Network-based Collaborative Filtering Method for Personalized Manufacturing Service Recommendation

Nowadays, social network-based collaborative filtering (CF) methods are widely applied to recommend suitable products to consumers by combining trust relationships and similarities in the preference ratings among past users. However, these types of methods are rarely used for recommending manufacturing services. Hence, this study has developed a hybrid social network-based CF method for recommending personalized manufacturing services. The trustworthy enterprises and three types of similar enterprises with different features were considered as the four influential components for calculating predicted ratings of candidate services. The stochastic approach for link structure analysis (SALSA) was adopted to select top K trustworthy enterprises while also considering their reputation propagation on enterprise social network. The predicted ratings of candidate services were computed by using an extended user-based CF method where the particle swarm optimization (PSO) algorithm was leveraged to optimize the weights of the four components, thus making service recommendation more objective. Finally, an evaluation experiment illustrated that the proposed method is more accurate than the traditional user-based CF method.

MUSE: asset risk scoring in enterprise network with mutually reinforced reputation propagation

Cyber security attacks are becoming ever more frequent and sophisticated. Enterprises often deploy several security protection mechanisms, such as anti-virus software, intrusion detection/prevention systems, and firewalls, to protect their critical assets against emerging threats. Unfortunately, these protection systems are typically ‘noisy’, e.g., regularly generating thousands of alerts every day. Plagued by false positives and irrelevant events, it is often neither practical nor cost-effective to analyze and respond to every single alert. The main challenges faced by enterprises are to extract important information from the plethora of alerts and to infer potential risks to their critical assets. A better understanding of risks will facilitate effective resource allocation and prioritization of further investigation. In this paper, we present MUSE, a system that analyzes a large number of alerts and derives risk scores by correlating diverse entities in an enterprise network. Instead of considering a risk as an isolated and static property pertaining only to individual users or devices, MUSE exploits a novel mutual reinforcement principle and models the dynamics of risk based on the interdependent relationship among multiple entities. We apply MUSE on real-world network traces and alerts from a large enterprise network consisting of more than 10,000 nodes and 100,000 edges. To scale up to such large graphical models, we formulate the algorithm using a distributed memory abstraction model that allows efficient in-memory parallel computations on large clusters. We implement MUSE on Apache Spark and demonstrate its efficacy in risk assessment and flexibility in incorporating a wide variety of datasets.

Domain-aware reputable service recommendation in heterogeneous manufacturing service ecosystem

Networked manufacturing becomes an important manufacturing method for modern manufacturing enterprises. With the wide adoption of service-oriented architecture and cloud manufacturing, manufacturing enterprises and organisations publish their manufacturing capability, such as resources, processes and knowledge as manufacturing services. A rapidly growing manufacturing service ecosystem can be observed nowadays, which brings the information overload problem for the service selection. Thus, how to organise these services and how to recommend the reputable services become two important issues for the manufacturing service retrieval and reuse. In this paper, the service cluster method WSTPCluster, which develops the topic model based on Latent Dirichlet Allocation, is employed to cluster services into specific domains. As the service ecosystem can be modelled as a heterogeneous service network, a unified reputation propagation method URPM is proposed to calculate the reputation of services and to distinguish the reputable services in each domain. Combining WSTPCluster and URPM, domain-aware reputable service recommendation method is introduced to recommend the high reputable services in each domain for the consumers. Experiments show that this method brings a better performance both in the recommendation accuracy and in the long tail recommendation.

Trust Model Based on Reputation Propagation Mechanism in Cloud Manufacturing Environment

Contrary to the issues of cloud manufacturing environment such as vandalism and fraud, the paper proposes a trust model based on reputation propagation mechanism according to the dynamic nature of the reputation model, given the network reputation spread and establish the appropriate calculation model; "dyadic reciprocity" method used in the calculation model, in combination with beta distribution and Chebyshev inequality to realize the reputation assessment between two nodes.

CRP: context-based reputation propagation in services composition

For a number of services with similar functionality reputation has been regarded as one of the most important methods to identify good ones from bad ones. However, a composite service, which is composed of multiple component services, obtains only one score (or feedback) after every invocation. In order to compute the reputation of each component service, it is necessary for the composite service to distribute this score to its component services. How to achieve a fair distribution is a challenging issue, as each component service may perform differently in contributing to the success or failure of the composite service. Although several efforts have been made for this problem, they do not consider the context of composition, which makes the distribution unfair. Therefore, in this paper, we propose a fair score distribution framework which combines the context of component services and their runtime performance. We distinguish two aspects contexts of a component service: structure-related importance and community-related replaceability, and adopt graph theory and dominating relationship technique to compute them, respectively. Experimental results show that our approach can achieve a more reasonable and fair score distribution than other existing methods.

Reputation Management for Composite Services in Service-Oriented Systems

This paper investigates the problem of reputation management in composite services. The authors propose a novel reputation propagation technique in composite services. The proposed approach provides a fair distribution of reputation values so that a component service is neither penalised nor awarded for the poor and good performance, respectively, of other peer component services. Experiment results indicate that the proposed technique propagates the “fair share” of reputation from the composite service to its component services.

Can reputation migrate? On the propagation of reputation in multi-context communities

As e-communities grow in both quality and quantity, their online users require more appropriate tools to suite their needs in such environments. Many such tools are not explicitly needed in real-world communities where humans directly interact with each other. Trust making and reputation ascription are among the most important examples of such tools. Humans often build trust relationships through interaction or recommendation, and are therefore able to ascribe relevant reputation to those they interact with. However, in online communities the process of trust making and reputation ascription is more complicated. In this paper, we address a special case of the trust making process where community users need to create bonds with those they have not encountered before. This is a common situation in websites such as amazon.com, ebay.com, epionions.com and many others. The model we propose is able to estimate the possible reputation of a given identity in a any new context by observing his/her behavior in other communities. Our proposed model employs Dempster–Shafer based valuation networks to develop a global reputation structure and performs a belief propagation technique to infer contextual reputation values. The preliminary evaluation of the proposed model on a dataset collected from epinions.com shows promising results.

A secure incentive architecture for ad hoc networks

AbstractIn an ad hoc network, intermediate nodes on a communication path are expected to forward packets of other nodes so that the mobile nodes can communicate beyond their wireless transmission range. However, because wireless mobile nodes are usually constrained by limited power and computation resources, a selfish node may be unwilling to spend its resources in forwarding packets which are not of its direct interest, even though it expects other nodes to forward its packets to the destination. It has been shown that the presence of such selfish nodes degrades the overall performance of a non‐cooperative ad hoc network. To address this problem, we propose a secure and objective reputation‐based incentive (SORI) architecture to encourage packet forwarding and discipline selfish behavior. Different from existing schemes, under our architecture, the reputation of a node is quantified by objective measures; the propagation of reputation is efficiently secured by a one‐way‐hash‐chain‐based authentication scheme; and secure routing is in place. Armed with the reputation‐based mechanism, we design a punishment scheme to penalize selfish nodes. The experimental results show that the proposed scheme can successfully identify selfish nodes and punish them accordingly. Copyright © 2006 John Wiley & Sons, Ltd.