SUNNY: a new algorithm for trust inference in social networks using probabilistic confidence models

Abstract

Figure 1: A sample social network with trust values (on a 0-1 scale) as edge labels. In many computing systems, information is produced and processed by many people. Knowing how much a user trusts a source can be very useful for aggregating, filtering, and ordering of information. Furthermore, if trust is used to support decision making, it is important to have an accurate estimate of trust when it is not directly available, as well as a measure of confidence in that estimate. This paper describes a new approach that gives an explicit probabilistic interpretation for confidence in social networks. We describe SUNNY, a new trust inference algorithm that uses a probabilistic sampling technique to estimate our confidence in the trust information from some designated sources. SUNNY computes an estimate of trust based on only those information sources with high confidence estimates. In our experiments, SUNNY produced more accurate trust estimates than the well known trust inference algorithm TIDALTRUST (Golbeck 2005), demonstrating its effectiveness.

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