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Trust in Web-based Social Networks

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The expansion of web-based social networking sites has resulted in completely new innovations in social networking, especially by permitting people to describe their associations beyond a basic connection. In this project, I look particularly at trust in web-based social networking sites, the way it can be calculated, and the way it could be utilized in applications. I start out with a concept of trust and an outline of many properties which affect how it’s utilized in algorithms. This really is accompanied by a survey of web-based social network sites to get an idea of their scope, the kinds of relationship information available, and the present state of trust. The computational problem of trust is to figure out how much one individual in the network should trust another individual with whom they’re not associated. I provide 2 groups of algorithms for determining these trust inferences: one for networks with binary trust ratings, the other for continuous ratings. For each rating scheme, the algorithms are designed upon…

Contents

1 Introduction
1.1 Contributions
1.2 Organization
2 Web-Based Social Networks
2.1 Introduction
2.2 Previous Work
2.3 Definitions
2.4 A Survey of Web-Based Social Networks
2.4.1 Size
2.4.2 Categorization
2.4.3 Relationship Data
2.5 The Semantic Web and Friend of a Friend (FOAF)
2.5.1 Background
2.5.2 FOAF and Current WBSNs
2.5.3 Extensions to FOAF
2.6 Conclusions and Future Directions
3 Trust: Definition and Properties
3.1 A Definition of Trust
3.2 Properties of Trust
3.2.1 Transitivity
3.2.2 Composability
3.2.3 Personalization and Asymmetry
3.3 The Values of Trust
3.4 Conclusions
4 Inferring Trust: Background and Related Work
4.1 From Trust Properties to Trust Algorithms
4.2 Previous Work
4.2.1 Game Theory
4.2.2 Peer-To-Peer Systems
4.2.3 Calculating Trust on the Web
4.2.4 Public Key Infrastructure
4.3 Conclusions
5 Inferring Trust in Binary Networks
5.1 Generating Social Networks
5.1.1 Building Networks with Correct Topology
5.1.2 Adding Trust Ratings to Graphs
5.2 Making Trust Inferences
5.2.1 A Rounding Algorithm
5.2.2 A Non-Rounding Algorithm
5.2.3 Analysis of the Algorithms
5.2.4 Simulations
5.3 Conclusions
6 Inferring Trust in Continuous Networks: TidalTrust
6.1 Experimental Networks
6.2 Patterns of Trust Values
6.2.1 Distribution of Trust Values
6.2.2 Correlation of Trust and Accuracy
6.2.3 Path Length and Accuracy
6.3 TidalTrust: An Algorithm for Inferring Trust
6.3.1 Incorporating Path Length
6.3.2 Incorporating Trust
6.3.3 Full Algorithm for Inferring Trust
6.4 Accuracy of TidalTrust
6.4.1 Discussion of Trust and Accuracy
6.4.2 Related Algorithms
6.5 Conclusions
7 Trust Inferences in Application: FilmTrust
7.1 Related Work
7.2 The FilmTrust Website
7.3 Site Personalization
7.3.1 Recommended Movie Ratings
7.3.2 Presenting Ordered Reviews
7.4 User Study
7.5 Conclusions and Discussion
8 TrustMail: Trust Networks for Email Filtering
8.1 Background and Introduction
8.2 The TrustMail Application
8.3 Case Study: The Enron Email Corpus
8.4 Conclusions
9 Conclusions
10 Future Work
10.1 Validation of Current Results
10.2 Extensions to Current Work
10.2.1 Network Structure and Trust Inferences
10.2.2 Recommendations with FilmTrust
10.2.3 Privacy……….

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Source: University of Maryland

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