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Models for Trust Inference in Social Networks

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Propagation Phenomena in Real World Networks

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 85))

Abstract

Interpersonal trust between any two people in social networks is hard to gauge, and even harder to infer, given that these two people are not connected by an immediate social link, such as friendship or acquaintanceship. In order to be able to make accurate inferences for an arbitrary tuple of people in a given social environment, we present an approach, named Appleseed, that is based on mechanics taken from neuropsychology, known as spreading activation models. Compelling in its simplicity, we relate the concept to trust propagation and evaluation in an intuitive fashion. While Appleseed works very well when paths between two arbitrary people in the network can be established, no inference of trust is possible when this is not the case. To this end, we present several algorithms for inferring trust that go beyond network structure and demonstrate their accuracy in real social networks. We also show how these algorithms can be augmented with additional data that may be available in some contexts.

The research presented in this chapter has been conducted while being affiliated with Albert-Ludwigs- Universität Freiburg i.Br., Germany.

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Notes

  1. 1.

    Note that trust concepts commonly adopted for webs of trust, and similar trust network applications, are largely general and do not cover specifics such as “situational trust” [26], as has been pointed out in [13]. For instance, agent \(a_i\) may blindly trust \(a_j\) with respect to books, but not trust \(a_j\) with respect to trusting others, for \(a_j\) has been found to accord trust to other people too easily. For our trust propagation scheme at hand, we also suppose this largely uni-dimensional concept of trust.

  2. 2.

    Supposing identical parameterizations for the metrics in use, as well as similar network structures.

  3. 3.

    Though various levels of peer certification exist, their interpretation does not perfectly align with weighted trust relationships.

  4. 4.

    With respect to seed node \(a\).

  5. 5.

    The terms “energy” and “trust” are used interchangeably in this context.

  6. 6.

    Crawls have been executed in September 2004.

  7. 7.

    We oversimplify by using predicate calculus expressions, supposing that trust, and hence distrust, is fully transitive.

  8. 8.

    See http://trust.mindswap.org/FilmTrust.

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Authors and Affiliations

  1. XING EVENTS GmbH, Sandstraße 33, 80335, Munich, Germany

    Cai-Nicolas Ziegler

  2. University of Maryland, College Park, MD, 20742, USA

    Jennifer Golbeck

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  1. Cai-Nicolas Ziegler
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  2. Jennifer Golbeck
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Correspondence to Cai-Nicolas Ziegler .

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Editors and Affiliations

  1. Department of Information Systems, Wrocław University of Technology, Wrocław, Poland

    Dariusz Król

  2. School of Design, Engineering and Computing, Bournemouth University, Fern Barrow Poole, United Kingdom

    Damien Fay

  3. Computational Intelligence Research Group, Bournemouth University Smart Technology Research Centre, Poole, United Kingdom

    Bogdan Gabryś

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Ziegler, CN., Golbeck, J. (2015). Models for Trust Inference in Social Networks. In: Król, D., Fay, D., Gabryś, B. (eds) Propagation Phenomena in Real World Networks. Intelligent Systems Reference Library, vol 85. Springer, Cham. https://doi.org/10.1007/978-3-319-15916-4_3

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  • DOI: https://doi.org/10.1007/978-3-319-15916-4_3

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