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On relational learning and discovery in social networks: a survey

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Abstract

The social networking scene has evolved tremendously over the years. It has grown in relational complexities that extend a vast presence onto popular social media platforms on the internet. With the advance of sentimental computing and social complexity, relationships which were once thought to be simple have now become multi-dimensional and widespread in the online scene. This explosion in the online social scene has attracted much research attention. The main aims of this work revolve around the knowledge discovery and datamining processes of these feature-rich relations. In this paper, we provide a survey of relational learning and discovery through popular social analysis of different structure types which are integral to applications within the emerging field of sentimental and affective computing. It is hoped that this contribution will add to the clarity of how social networks are analyzed with the latest groundbreaking methods and provide certain directions for future improvements.

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Notes

  1. URL: http://www.sics.se/humle/projects/prothalt/.

  2. URL: http://ftp.cs.utexas.edu/mooney/bio-data/.

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  1. Faculty of Health Engineering and Sciences, University of Southern Queensland, Darling Heights, QLD, Australia

    Ji Zhang, Leonard Tan & Xiaohui Tao

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  1. Ji Zhang
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Zhang, J., Tan, L. & Tao, X. On relational learning and discovery in social networks: a survey. Int. J. Mach. Learn. & Cyber. 10, 2085–2102 (2019). https://doi.org/10.1007/s13042-018-0823-8

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