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Graph neural network-based attention mechanism to classify spam review over heterogeneous social networks

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Abstract

Graph Neural Networks (GNNs) models, a current machine learning hotspot, have increasingly started to be applied in fraud detection in conjunction with user reviews in recent years. The accessible material is complicated and varied, the aggregated user evaluations cover a diverse range of topics, and erroneous information among vast amounts of user-generated content is typically rare. The review system is modeled as a heterogeneous network to address the issue of feature heterogeneity and uneven data distribution, and a new social theory-based graphical neural network model (SGNN) is suggested. The rich user behavior information in the heterogeneous network may be fully leveraged to acquire richer semantic representations for comments by integrating the hierarchical attention structure. Under the ensemble learning bagging framework, various distinct SGNN sub-models are combined. The sampling technique realizes the diversity aggregation of the base learners, which reduces the loss of useful information and improves the ability to identify bogus comments. According to testing results on real datasets from Amazon and YelpChi, the SGNN approach provides strong anomaly detection performance. It is demonstrated that the SGNN process has good robustness against fraudulent entities in the use of skewed distribution of data categories when compared to the existing approach.

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  1. National Institute of Technical Teachers’ Training and Research, Bhopal, India

    Monti Babulal Pal & Sanjay Agrawal

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Monti Babulal Pal contributed to the conceptualization, investigation, methodology development, and initial drafting of the manuscript. Sanjay Agrawal provided supervision, resources, critical review, and editing of the manuscript.

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Correspondence to Monti Babulal Pal.

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Pal, M.B., Agrawal, S. Graph neural network-based attention mechanism to classify spam review over heterogeneous social networks. J Supercomput 80, 27176–27203 (2024). https://doi.org/10.1007/s11227-024-06459-1

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