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DeciTrustNET: A graph based trust and reputation framework for social networks

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Abstract

The world wide success of large scale social information systems with diverse purposes, such as e-commerce platforms, facilities sharing communities and social networks, make them a very promising paradigm for large scale information sharing and management. However the anonymity, distributed and open nature of these frameworks, that, on the one hand, foster the communication capabilities of their users, may contribute, on the other hand, to the propagation of low quality information, attacks and manipulations from users with malicious intentions. All of these risks could end up decreasing users’ confidence in these systems and in a reduction of their utilisation. With these issues in mind, the objective of this contribution is to create DeciTrustNET, a trust and reputation based framework for social networks that takes into consideration the users relationships, the historic evolution of their reputations and their profile similarity to develop a tamper resilient network that guarantees trustworthy communications and transactions. An extensive experimental analysis of the developed framework has been carried out confirming that the proposed approach supports robust trust and reputation establishment among the users, even in social network under the presence of malicious users.

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... For example, Martín and colleagues (Martín et al., 2021) designed a system that supports human experts to detect misinformation by extracting relevant semantic and sentiment features from the articles. Related to this, Urena et al. (2020) designed a trust and reputation estimation framework for social media that considers network-based and temporal features such as users relationships or the evolution of reputation. ...
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A (directed) network of people connected by ratings or trust scores, and a model for propagating those trust scores, is a fundamental building block in many of today's most successful e-commerce and recommendation systems. We develop a framework of trust propagation schemes, each of which may be appropriate in certain circumstances, and evaluate the schemes on a large trust network consisting of 800K trust scores expressed among 130K people. We show that a small number of expressed trusts/distrust per individual allows us to predict trust between any two people in the system with high accuracy. Our work appears to be the first to incorporate distrust in a computational trust propagation setting.
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A general coefficient measuring the similarity between two sampling units is defined. The matrix of similarities between all pairs of sample units is shown to be positive semidefinite (except possibly when there are missing values). This is important for the multidimensional Euclidean representation of the sample and also establishes some inequalities amongst the similarities relating three individuals. The definition is extended to cope with a hierarchy of characters.
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Reputations that are transmitted from person to person can deter moral hazard and discourage entry by bad types in markets where players repeat transactions but rarely with the same player. On the Internet, information about past transactions may be both limited and potentially unreliable, but it can be distributed far more systematically than the informal gossip among friends that characterizes conventional marketplaces. One of the earliest and best known Internet reputation systems is run by eBay, which gathers comments from buyers and sellers about each other after each transaction. Examination of a large data set from 1999 reveals several interesting features of this system, which facilitates many millions of sales each month. First, despite incentives to free ride, feedback was provided more than half the time. Second, well beyond reasonable expectation, it was almost always positive. Third, reputation profiles were predictive of future performance. However, the net feedback scores that eBay displays encourages Pollyanna assessments of reputations, and is far from the best predictor available. Fourth, although sellers with better reputations were more likely to sell their items, they enjoyed no boost in price, at least for the two sets of items that we examined. Fifth, there was a high correlation between buyer and seller feedback, suggesting that the players reciprocate and retaliate.
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We introduce the induced ordered weighted averaging (IOWA) operator. In these operators the argument ordering process is guided by a variable called the order inducing value. A procedure for learning the weights from data is described. We suggest a number of applications of these induced OWA aggregation operators. First we show its possibilities in modeling nearest-neighbor rules. Next it is applied to the aggregation of complex objects such as matrices. It is also used to establish a new class of information fusion models called “best yesterday models”. Finally, we extend the idea of order induced aggregation to the Choquet aggregation resulting in what we call the induced Choquet ordered averaging (I-COA) operator.
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The intuitive background for measures of structural centrality in social networks is reviewed and existing measures are evaluated in terms of their consistency with intuitions and their interpretability.Three distinct intuitive conceptions of centrality are uncovered and existing measures are refined to embody these conceptions. Three measures are developed for each concept, one absolute and one relative measure of the centrality of positions in a network, and one reflecting the degree of centralization of the entire network. The implications of these measures for the experimental study of small groups is examined.
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Trust and reputation systems represent a significant trend in decision support for Internet mediated service provision. The basic idea is to let parties rate each other, for example after the completion of a transaction, and use the aggregated ratings about a given party to derive a trust or reputation score, which can assist other parties in deciding whether or not to transact with that party in the future. A natural side effect is that it also provides an incentive for good behaviour, and therefore tends to have a positive effect on market quality. Reputation systems can be called collaborative sanctioning systems to reflect their collaborative nature, and are related to collaborative filtering systems. Reputation systems are already being used in successful commercial online applications. There is also a rapidly growing literature around trust and reputation systems, but unfortunately this activity is not very coherent. The purpose of this article is to give an overview of existing and proposed systems that can be used to derive measures of trust and reputation for Internet transactions, to analyse the current trends and developments in this area, and to propose a research agenda for trust and reputation systems.
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The importance of a Web page is an inherently subjective matter, which depends on the readers interests, knowledge and attitudes. But there is still much that can be said objectively about the relative importance of Web pages. This paper describes PageRank, a mathod for rating Web pages objectively and mechanically, effectively measuring the human interest and attention devoted to them. We compare PageRank to an idealized random Web surfer. We show how to efficiently compute PageRank for large numbers of pages. And, we show how to apply PageRank to search and to user navigation.
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In this paper, the authors present an empirical evaluation of similarity coefficients for binary valued data. Similarity coefficients provide a means to measure the similarity or distance between two binary valued objects in a dataset such that the attributes qualifying each object have a 0-1 value. This is useful in several domains, such as similarity of feature vectors in sensor networks, document search, router network mining, and web mining. The authors survey 35 similarity coefficients used in various domains and present conclusions about the efficacy of the similarity computed in (1) labeled data to quantify the accuracy of the similarity coefficients, (2) varying density of the data to evaluate the effect of sparsity of the values, and (3) varying number of attributes to see the effect of high dimensionality in the data on the similarity computed.
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Reputation systems provide mechanisms to produce a metric encapsulating reputation for a given domain for each identity within the system. These systems seek to generate an accurate assessment in the face of various factors including but not limited to unprecedented community size and potentially adversarial environments. We focus on attacks and defense mechanisms in reputation systems. We present an analysis framework that allows for the general decomposition of existing reputation systems. We classify attacks against reputation systems by identifying which system components and design choices are the targets of attacks. We survey defense mechanisms employed by existing reputation systems. Finally, we analyze several landmark systems in the peer-to-peer domain, characterizing their individual strengths and weaknesses. Our work contributes to understanding (1) which design components of reputation systems are most vulnerable, (2) what are the most appropriate defense mechanisms and (3) how these defense mechanisms can be integrated into existing or future reputation systems to make them resilient to attacks.
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Social information systems are a promising new paradigm for large-scale distributed information management, as evidenced by the success of large-scale information sharing communities, social media sites, and web-based social networks. But the increasing reliance on these social systems also places individuals and their computer systems at risk, creating opportunities for malicious participants to exploit the tight social fabric of these networks. With these problems in mind, this manuscript presents the SocialTrust framework for enabling trusted social information management in Internet-scale social information systems. Concretely, we study online social networks, consider a number of vulnerabilities inherent in online social networks, and introduce the SocialTrust framework for supporting tamper-resilient trust establishment. We study three key factors for trust establishment in online social networks – trust group feedback, distinguishing the user’s relationship quality from trust, and tracking user behavior – and describe a principled approach for assessing each component. In addition to the SocialTrust framework, which provides a network-wide perspective on the trust of all users, we describe a personalized extension called mySocialTrust, which provides a user-centric trust perspective that can be optimized for individual users within the network. Finally, we experimentally evaluate the SocialTrust framework using real online social networking data consisting of millions of MySpace profiles and relationships. While other trust aggregation approaches have been developed and implemented by others, we note that it is rare to find such a large-scale experimental evaluation that carefully considers the important factors impacting the trust framework. We find that SocialTrust supports robust trust establishment even in the presence of large-scale collusion by malicious participants.
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Eigenvectors of adjacency matrices are useful as measures of centrality or of status. However, they are misapplied to asymmetric networks in which some positions are unchosen. For these networks, an alternative measure of centrality is suggested that equals an eigenvector when eigenvectors can be used and provides meaningfully comparable results when they cannot.
Conference Paper
A (directed) network of people connected by ratings or trust scores, and a model for propagating those trust scores, is a fundamental building block in many of today's most successful e-commerce and recommendation systems. We develop a framework of trust propagation schemes, each of which may be appropriate in certain circumstances, and evaluate the schemes on a large trust network consisting of 800K trust scores expressed among 130K people. We show that a small number of expressed trusts/distrust per individual allows us to predict trust between any two people in the system with high accuracy. Our work appears to be the first to incorporate distrust in a computational trust propagation setting.
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Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.