为了基于图表的欺诈检测,重新审视低同志关系.
Tairan Huang1, Qiutong Li1, Cong Xu1
1School of Computer Science and Engineering, Central South University, Changsha, China.
概括
本研究介绍了HeteGAD,这是一个用于欺诈检测的新型图形神经网络 (GNN) 框架. HeteGAD有效地平衡同型和异型,在现实世界数据集上表现优于现有方法.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 数据挖掘 数据挖掘
背景情况:
- 在线欺诈构成重大威胁,需要先进的检测方法.
- 图形神经网络 (GNN) 利用图形结构进行欺诈检测,但与欺诈者典型的异性恋联系作斗争.
- 现有的GNN往往无法有效利用异构性,限制了其在欺诈检测方面的表现.
研究的目的:
- 开发一个新的框架,HeteGAD,通过有效处理同型和异型图形结构来解决传统GNN在欺诈检测方面的局限性.
- 通过明确模拟同型和异型之间的相互作用来提高欺诈检测系统的准确性和稳定性.
主要方法:
- 提出了HeteGAD,这是一个综合框架,有两个核心策略:异性恋意识聚合和同性恋意识聚合.
- 意识到异性恋的聚合策略增强了异性恋邻居的特征差异,并增加了阶级间的隔离.
- 同性恋意识的聚合策略捕获了全球同性恋信息,并增加了同样标记的节点的表示相似性. 嵌入式的相互关系的注意力机制完善了多关系建模.
主要成果:
- 与11种最先进的基线方法相比,HeteGAD在欺诈检测任务中表现优越.
- 在两个真实世界数据集上的实验验验证了拟议框架在平衡同类性和异类性方面的有效性.
- 该框架成功地缩小了阶级内部的距离,并增加了阶级间的隔离,这对于区分欺诈者至关重要.
结论:
- 在基于图表的欺诈检测方面,HeteGAD通过有效整合同型和异型信息,提供了显著的进步.
- 拟议的方法为在复杂的网络结构中识别欺诈活动提供了更强大,更准确的解决方案.
- 这项工作强调了考虑同型性和异型性对于在现实世界欺诈检测场景中提高GNN性能的重要性.
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