Graph Neural Network with One-side Edge Sampling for Fraud Detection

• URL: http://arxiv.org/abs/2601.06800v1
• Date: Sun, 11 Jan 2026 07:59:39 GMT
• Title: Graph Neural Network with One-side Edge Sampling for Fraud Detection
• Authors: Hoang Hiep Trieu,
• Abstract summary: One-Side Edge Sampling (OES) can potentially reduce training duration as well as the effects of over-smoothing and over-fitting.<n>OES can empirically outperform backbone models in both shallow and deep architectures.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Financial fraud is always a major problem in the field of finance, as it can cause significant consequences. As a result, many approaches have been designed to detect it, and lately Graph Neural Networks (GNNs) have been demonstrated as a competent candidate. However, when trained with a large amount of data, they are slow and computationally demanding. In addition, GNNs may need a deep architecture to detect complex fraud patterns, but doing so may make them suffer from problems such as over-fitting or over-smoothing. Over-fitting leads to reduced generalisation of the model on unseen data, while over-smoothing causes all nodes' features to converge to a fixed point due to excessive aggregation of information from neighbouring nodes. In this research, I propose an approach called One-Side Edge Sampling (OES) that can potentially reduce training duration as well as the effects of over-smoothing and over-fitting. The approach leverages predictive confidence in an edge classification task to sample edges from the input graph during a certain number of epochs. To explain why OES can alleviate over-smoothing, I perform a theoretical analysis of the proposed approach. In addition, to validate the effect of OES, I conduct experiments using different GNNs on two datasets. The results show that OES can empirically outperform backbone models in both shallow and deep architectures while also reducing training time.

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