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Enhancing Trust Prediction in Attributed Social Networks with Self-Supervised Learning

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Web Information Systems Engineering – WISE 2023 (WISE 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14306))

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Abstract

Predicting trust in Online Social Networks (OSNs) is essential for a range of applications including online marketing and decision-making. Traditional methods, while effective in some scenarios, encounter difficulties when attempting to handle the complexities of trust networks and the sparsity of trust relationships. Current techniques attempt to use user attributes such as ratings and reviews to fill these data gaps, although this approach can introduce noise and compromise prediction accuracy. A significant problem remains: most users do not explicitly state their trust relationships, making it difficult to infer trust from a vast amount of unlabelled data. This paper introduces a novel model, Trust Network Prediction (TNP), which employs self-supervised learning to address these issues within attributed trust networks. TNP learns efficiently from unlabelled data, enabling the inference of potential trust connections even without explicit trust relationships. It also minimises redundancy and the impact of abundant unlabelled data by generating comprehensive user representations based on existing trust relationships and reviewing behaviour. Through comprehensive testing on two real-world datasets, our proposed model demonstrates its effectiveness and reliability in trust prediction tasks, underscoring its potential utility in OSNs.

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Notes

  1. 1.

    https://www.cse.msu.edu/~tangjili/trust.html.

  2. 2.

    https://pytorch.org/.

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Authors and Affiliations

  1. School of Computing, Macquarie University, Sydney, NSW, 2109, Australia

    Hongjiao Liu, Shan Xue, Jian Yang & Jia Wu

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  1. Hongjiao Liu
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  2. Shan Xue
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  3. Jian Yang
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  4. Jia Wu
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Corresponding author

Correspondence to Hongjiao Liu .

Editor information

Editors and Affiliations

  1. Renmin University of China, Beijing, China

    Feng Zhang

  2. Victoria University, Footscray, VIC, Australia

    Hua Wang

  3. Qatar University, Doha, Qatar

    Mahmoud Barhamgi

  4. Swinburne University of Technology, Hawthorn, Australia

    Lu Chen

  5. Swinburne University of Technology, Hawthorn, Australia

    Rui Zhou

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Liu, H., Xue, S., Yang, J., Wu, J. (2023). Enhancing Trust Prediction in Attributed Social Networks with Self-Supervised Learning. In: Zhang, F., Wang, H., Barhamgi, M., Chen, L., Zhou, R. (eds) Web Information Systems Engineering – WISE 2023. WISE 2023. Lecture Notes in Computer Science, vol 14306. Springer, Singapore. https://doi.org/10.1007/978-981-99-7254-8_13

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  • DOI: https://doi.org/10.1007/978-981-99-7254-8_13

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  • Online ISBN: 978-981-99-7254-8

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