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Trust prediction in online social networks

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posted on 29.03.2022, 03:01 by Xiaoming Zheng
Online social networks (OSNs) have become an integral part of daily life in recent years. They have been used as a means for a rich variety of activities, such as seeking service providers or recommendations. In these activities, trust is one of the most important factors for participants' decision-making process. Therefore, it is necessary and significant to predict the trust between two participants who have no direct interactions. My thesis aims to provide effective and efficient trust prediction approaches to evaluate trust values, which are introduced from the following four aspects. The first aspect of the work is to study the factors that affect trust in OSNs andsolve the trust network extraction problem. OSNs contain important participants, thetrust relations between participants, and the contexts in which participants interact with each other. All of such information has a significant influence on the prediction of the trust from a source participant to a target participant without direct interactions.In addition, the trust network, containing a truster and a trustee without direct interactions,is the foundation to perform trust prediction. The extraction of a small-scaletrust subnetwork can deliver efficient and effective trust prediction results. We proposetwo heuristic algorithms called NBACA and NACA for the extraction of such subnetworks. The second aspect of the work is to address the trust prediction problem in the trustnetwork without any contextual information. We first analyze and extract the featureswhich affect the trust prediction from trust rating values in a trust network. Then, anew trust prediction model based on trust decomposition and matrix factorization is proposed to predict the trust value from a truster to a trustee. In this model, trust is first decomposed into trust tendency and tendency-reduced trust. Based on tendency reducedtrust ratings, matrix factorization with a regularization term is leveraged to predict the tendency-reduced values of missing trust ratings, incorporating both propagated trust and the similarity of users’ rating habits. Finally, the missing trust ratings are composed with predicted tendency-reduced values and trust tendency values. The third aspect of the work is to study the trust prediction problem in OSNs with social contextual information. We first categorize the factors that affect trust, and utilize them according to their categories, to transfer or calculate existing trust values.Then, a new trust transference method is proposed to predict the trust in a target context from that in different but relevant contexts. Next, a social context-aware trust prediction model based on matrix factorization is proposed to predict trust from asource participant to a target participant in various situations. Finally, we analyze thecontextual trust prediction in three common scenarios. The fourth aspect of the work is to study the dynamic trust to online service providers to assist the decision making regarding a future interaction. First, staticfeatures and dynamic features are extracted from historical interaction records. Then,Principal Component Analysis and Vector Quantization techniques are leveraged to reduce the dimension of features and project them into discrete values. Last, an approach based on Hidden Markov Model is proposed to model the dynamic changes of trust, and to predict the trust in the future interactions. For all the proposed approaches, extensive experiments have been conducted or analyzed on real datasets, semi-synthetic datasets, synthetic datasets or real scenarios,which demonstrates that they are superior to the exiting approaches in terms of quality of results and efficiency.


Table of Contents

1. Introduction -- 2. Literature review -- Subnetwork extraction in trust social networks -- 4. Single-context trust prediction in OSNs -- 5. Social context-aware trust prediction in OSNs -- 6. Dynamic trust prediction of online environments -- 7. Conclusions and future work.


Bibliography: pages 151-176 Theoretical thesis.

Awarding Institution

Macquarie University

Degree Type

Thesis PhD


PhD, Macquarie University, Faculty of Science and Engineering, Department of Computing

Department, Centre or School

Department of Computing

Year of Award


Principal Supervisor

Mehmet A. Orgun

Additional Supervisor 1

Yan Wang


Copyright Xiaoming Zheng 2015. Copyright disclaimer: http://www.copyright.mq.edu.au




1 online resource (xviii, 176 pages)

Former Identifiers

mq:44535 http://hdl.handle.net/1959.14/1070090