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Calculating trust using multiple heterogeneous social networks

Calculating trust using multiple heterogeneous social networks
Calculating trust using multiple heterogeneous social networks
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 needs to be consolidated. It is non-trivial 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 preserves 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 which 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 that 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 non-overlapping regions of the social networks.
Imran, Muhammad
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Khattak, Hasan Ali
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Millard, David
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Tiropanis, Thanassis
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Bashir, Tariq
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Ahmed, Ghufran
3500d942-b95a-47fc-9b44-ff439f1d2afd
Imran, Muhammad
82d0aef7-1790-4a8a-8277-2415b18a463f
Khattak, Hasan Ali
a491b626-1d00-477f-8b8e-8b074822a211
Millard, David
4f19bca5-80dc-4533-a101-89a5a0e3b372
Tiropanis, Thanassis
d06654bd-5513-407b-9acd-6f9b9c5009d8
Bashir, Tariq
30510301-032d-4509-8f16-5dcc563b1f9d
Ahmed, Ghufran
3500d942-b95a-47fc-9b44-ff439f1d2afd

Imran, Muhammad, Khattak, Hasan Ali, Millard, David, Tiropanis, Thanassis, Bashir, Tariq and Ahmed, Ghufran (2020) Calculating trust using multiple heterogeneous social networks. Wireless Communications and Mobile Computing Journal, 2020, [8545128]. (doi:10.1155/2020/8545128).

Record type: Article

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 needs to be consolidated. It is non-trivial 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 preserves 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 which 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 that 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 non-overlapping regions of the social networks.

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Accepted/In Press date: 24 January 2020
e-pub ahead of print date: 21 February 2020

Identifiers

Local EPrints ID: 437594
URI: http://eprints.soton.ac.uk/id/eprint/437594
PURE UUID: 034fa6cb-db27-400e-b63f-7f245bc7263c
ORCID for David Millard: ORCID iD orcid.org/0000-0002-7512-2710
ORCID for Thanassis Tiropanis: ORCID iD orcid.org/0000-0002-6195-2852

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Date deposited: 06 Feb 2020 17:31
Last modified: 09 Oct 2020 04:01

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Contributors

Author: Muhammad Imran
Author: Hasan Ali Khattak
Author: David Millard ORCID iD
Author: Thanassis Tiropanis ORCID iD
Author: Tariq Bashir
Author: Ghufran Ahmed

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