Understanding Social Networks using Transfer Learning

TL;DR

This paper explores how transfer learning can be applied to analyze users on new Web platforms, introducing TraNet, which effectively transfers user knowledge across different platforms for tasks like trust and role identification.

Contribution

The paper proposes TraNet, a transfer learning approach tailored for studying users on emerging Web platforms, demonstrating its effectiveness over other methods.

Findings

TraNet outperforms other approaches in cross-platform user analysis.

Transfer learning improves understanding of users on new Web platforms.

TraNet effectively transfers knowledge for trust and role identification tasks.

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.