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.

Jun Sun, Steffen Staab, and Jérôme Kunegis, "Understanding Social Networks Using Transfer Learning", IEEE Computer Magazine, Vol. 51, No 6, pp. 52-60, June 2018. (DOI: https://doi.ieeecomputersociety.org/10.1109/MC.2018.2701640)