Calculating trust using multiple heterogeneous social networks

Abstract

In today's Internet, a web user becomes members of multiple social networks due to different types of services provided by each of these networks. This creates an opportunity to make trust decisions that go beyond individual social networks, since these networks provide single perspective of trust. To make trust inference over multiple social networks, these networks need to be consolidated. It is nontrivial as these networks are of heterogeneous nature due to different naming conventions used in these networks. Furthermore, trust metrics extracted from these networks are also varied in nature due to different trust evaluation algorithms used in each of these networks. Heterogeneity of these social networks can be overcome by using semantic technologies as it allows us to represent knowledge using ontologies. Trust data can be consolidated by using such data fusion techniques which not only provide but also preserve trust data integrity from each of the individual social network profiles. The proposed semantic framework is evaluated using two sets of experiments. Through simulations in this work, we analysed various techniques for data fusion. For identifying suitable technique that preserves the integrity of trust consolidated from each of the individual networks, analysis revealed that Weighted Ordered Weighted Averaging parameter best aggregated trust data, and, unlike other techniques, it preserved the integrity of trust from each individual network for varying participant overlap and tie overlap (p≤0.05). Similarly, for experimental analysis, we used findings of the simulation study about the best trust aggregation technique and applied the proposed framework on real-life trust data between participants, which we extracted from pairs of professional social networks. Analysis partially proved our hypothesis about generating better trust values from consolidated multiple heterogeneous networks. We witnessed an improvement in overall results for all the participants who were part of multiple social networks (p≤0.05), while disproving the claim for those existing in nonoverlapping regions of the social networks.

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