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一种基于图形转换的不平衡学习方法,用于欺诈检测和检测欺诈.

Jintao Wen1, Xianghong Tang2,3, Jianguang Lu1,4

  • 1College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China.

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概括

本研究介绍了基于图形的Trans-SMOTE (GTS) 以有效检测欺诈行为. GTS增强了特征表示,解决了类不平衡,在现实数据集上表现优于现有方法.

关键词:
功能提取 功能提取欺诈检测和发现欺诈行为节点嵌入 节点嵌入过量采样方法 过量采样方法亚图的结构是分图的结构.

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科学领域:

  • 计算机科学 计算机科学
  • 数据科学数据科学数据科学

背景情况:

  • 欺诈对个人和社会稳定构成重大风险,需要先进的检测方法.
  • 在社交媒体中,欺诈者是一个稀缺的少数群体,经常组成小团体,这挑战了传统的图形神经网络.
  • 现有的图形神经网络因欺诈节点的稀缺性而难以充分表现欺诈特征.

研究的目的:

  • 提出一种新的方法,即基于图形的Trans-SMOTE (GTS),用于更好地检测图形结构数据中的欺诈行为.
  • 通过整合结构和属性特征来提高欺诈特征的表现.
  • 为了减轻欺诈检测场景中固有的阶级不平衡问题.

主要方法:

  • 使用子图神经网络提取器来深入挖掘结构节点特征.
  • 使用变压器技术集成结构和属性特征,以丰富节点表示.
  • 使用嵌入空间和边缘生成器的功能来创建合成少数类节点,解决类不平衡.

主要成果:

  • 与最先进的基线相比,拟议的GTS方法显示出更高的性能.
  • 在两个真实数据集上的实验验验证了GTS在欺诈检测方面的有效性.
  • GTS成功地解决了不充分的特征表示和类不平衡问题.

结论:

  • GTS为欺诈检测提供了强大的解决方案,特别是在不平衡的图形数据中.
  • 变压器技术和合成数据生成的整合大大提高了检测准确度.
  • 这种方法为基于图表的欺诈检测的未来研究提供了有希望的方向.