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Understanding social networks using transfer learning

Understanding social networks using transfer learning
Understanding social networks using transfer learning
A detailed understanding of users contributes to the understanding of the Web’s evolution, and to the development of Web applications. Although for new Web platforms such a study is especially important, it is often jeopardized by the lack of knowledge about novel phenomena due to the sparsity of data. Akin to human transfer of experiences from one domain to the next, transfer learning as a subfield of machine learning adapts knowledge acquired in one domain to a new domain. We systematically investigate how the concept of transfer learning may be applied to the study of users on newly created (emerging) Web platforms, and propose our transfer learning–based approach, TraNet. We show two use cases where TraNet is applied to tasks involving the identification of user trust and roles on different Web platforms. We compare the performance of TraNet with other approaches and find that our approach can best transfer knowledge on users across platforms in the given tasks.
Web Science, Internet, transfer learning, machine learning, artificial intelligence (AI), intelligent systems, human-computer interaction, social networks
Sun, Jun
cbc6b83e-3571-4f6a-b77d-51a8a20ac839
Staab, Steffen
bf48d51b-bd11-4d58-8e1c-4e6e03b30c49
Kunegis, Jérôme
066b7173-f5a6-4a0e-9656-873af0821799
Sun, Jun
cbc6b83e-3571-4f6a-b77d-51a8a20ac839
Staab, Steffen
bf48d51b-bd11-4d58-8e1c-4e6e03b30c49
Kunegis, Jérôme
066b7173-f5a6-4a0e-9656-873af0821799

Sun, Jun, Staab, Steffen and Kunegis, Jérôme (2018) Understanding social networks using transfer learning. IEEE Computer, 51 (6).

Record type: Article

Abstract

A detailed understanding of users contributes to the understanding of the Web’s evolution, and to the development of Web applications. Although for new Web platforms such a study is especially important, it is often jeopardized by the lack of knowledge about novel phenomena due to the sparsity of data. Akin to human transfer of experiences from one domain to the next, transfer learning as a subfield of machine learning adapts knowledge acquired in one domain to a new domain. We systematically investigate how the concept of transfer learning may be applied to the study of users on newly created (emerging) Web platforms, and propose our transfer learning–based approach, TraNet. We show two use cases where TraNet is applied to tasks involving the identification of user trust and roles on different Web platforms. We compare the performance of TraNet with other approaches and find that our approach can best transfer knowledge on users across platforms in the given tasks.

Text CO_COMSI-2017-10-0172.R1_Sun [ACK CUTLER]-revised-submission - Accepted Manuscript
Restricted to Repository staff only until 30 June 2018.
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More information

Accepted/In Press date: 4 May 2018
Published date: 30 June 2018
Keywords: Web Science, Internet, transfer learning, machine learning, artificial intelligence (AI), intelligent systems, human-computer interaction, social networks

Identifiers

Local EPrints ID: 421084
URI: https://eprints.soton.ac.uk/id/eprint/421084
PURE UUID: d073e43d-a0b7-4a80-aa8f-b9ab319d93f6
ORCID for Steffen Staab: ORCID iD orcid.org/0000-0002-0780-4154

Catalogue record

Date deposited: 22 May 2018 16:30
Last modified: 06 Jun 2018 12:21

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Contributors

Author: Jun Sun
Author: Steffen Staab ORCID iD
Author: Jérôme Kunegis

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