The impact of consolidating web based social networks on trust metrics and expert recommendation systems

Abstract

Individuals are typically members of a variety of web-based social networks (both explicit and implied), but existing trust inference mechanisms typically draw on only a single
network to calculate trust between any two individuals. This reduces both the likelihood that a trust value can be calculated (as both people have to be members of the same
network), and the quality of any trust inference that can be drawn (as it will be based on only a single network, typically representing a single type of relationship). To make trust calculations on Multiple Distributed (MuDi) social networks, those networks must first be consolidated into a single network.

Two challenges that arise when consolidating MuDi networks are their heterogeneity, due to different name representation techniques used for participants, and the variability of trust information, due to the different trust evaluation criteria, across the different candidate networks. Semantic technologies are vital to deal with the heterogeneity issues as they permit data to be linked from multiple resources and help them to be modelled in a uniform representation using ontologies. The inconsistency of multiple trust values from different networks is handled using data fusion techniques, as simpler aggregation
techniques of summation and weighted averages tend to distort trust data.

To test the proposed semantic framework, two set of experiments were run. Simulation experiments generated pairs of networks with varying percentages of Participant Overlap (PO) and Tie Overlap (TO), with trust values added to the links between participants in the networks. It analysed different data fusion techniques aiming to identify which best preserved the integrity of trust from each individual network with varying values of PO and TO. A real world experiment used the findings of the simulation experiment on the best trust aggregation techniques and applied the framework to real trust data between participants that was extracted from a pair of professional social networks. The trust values generated from consolidated MuDi networks were then compared with the
real life trust between users, collected using a survey, with the aim of analysing whether aggregated trust is closer to real life trust than using each of the individual networks.

Analysis of the simulation experiment showed that the Weighted Ordered Weighted Averaging (WOWA) data fusion technique better aggregated trust data and, unlike the
other techniques, preserved the integrity of trust from each individual network for varying PO and TO (p ? 0.05). The real world experiment partially proved the hypothesis of
generating better trust values from consolidated MuDi networks and showed improved results for participants who are part of both networks (p ? 0.05), while disproving the
claim for those in the cross-region (with one user present in both networks and the other in a single network) and single-network users (p > 0.05).

More information

Identifiers

ORCID for David Millard: orcid.org/0000-0002-7512-2710

Catalogue record

Export record