图对比学习作为一种有效的预训练策略，能够解决基于图的欺诈检测方法中高质量标签数据匮乏的问题。然而，当前这类方法面临恶意行为特征在图神经网络聚合机制中被削弱或在数据增强过程中受损的挑战。为此，提出了一种结合图重构和动态数据增强技术的图对比学习优化方法，旨在提升欺诈检测的效果。该方法通过调整图的边权重，减少因邻居特征聚合而产生的冲突，从而提高检测准确性。同时，利用标签不变性和分布多样性指标动态调整数据增强过程，以确保增强数据既能保留关键的欺诈特征，又具备必要的多样性。在多个图欺诈检测数据集上的实验结果表明了该方法的有效性，相较于最先进的方法，检测性能提升了2%~5%。

Graph contrastive learning, as an effective pre-training strategy, can address the issue of scarce high-quality labeled data in graph-based fraud detection methods. However, current approaches in this category face challenges where malicious behavioral features are either weakened within the aggregation mechanism of graph neural networks or compromised during the data augmentation process. To tackle this, this paper proposes an optimized graph contrastive learning method that integrates graph reconstruction and dynamic data augmentation techniques, aiming to enhance the effectiveness of fraud detection. This method reduces conflicts arising from neighbor feature aggregation by adjusting edge weights in the graph, thereby improving detection accuracy. Simultaneously, it dynam- ically adjusts the data augmentation process using label invariance and distribution diversity metrics to ensure that the augmented data retains critical fraud features while possessing necessary diversity. Experimental results on multiple graph fraud detection datasets demonstrate the effectiveness of this method,  with detection performance improvements ranging from 2% to 5% compared to state-of-the-art methods.