Practical aggregation operators for gradual trust and distrust

Trust and distrust are two increasingly important metrics in social networks, reflecting users' attitudes and relationships towards each other. In this paper, we study the indirect derivation of these metrics' values for users that do not know each other, but are connected through the network. In particular, we study bilattice-based aggregation approaches and investigate how they can be improved by using ordered weighted averaging techniques, or through the incorporation of knowledge defects. Experiments on a real world data set from CouchSurfing.org demonstrate that the best operators from a theoretical perspective are not always the most suitable ones in practice, and that the sophisticated aggregation methods can outperform the more obvious bilattice-based approaches.