利用GraphConvolution网络识别关联欺诈行为
Identifying Linked Fraudulent Activities Using GraphConvolution Network
摘要：
在本文中，我们提出了一种新的方法来识别链接的欺诈活动或演员共享相似的属性，使用图卷积网络（GCN）。这些关联的欺诈活动可以被可视化为具有关系和交互等抽象概念的图，这使得GCN成为识别作为欺诈节点之间链接的图边缘的理想解决方案。传统的方法，如社区发现，需要像共享属性的欺诈尝试之间的强链接，以找到社区和监督的解决方案需要大量的训练数据，这可能是不可用的欺诈场景，并最好地工作，以提供欺诈和非欺诈活动之间的二元分离。我们的方法克服了传统方法的缺点，因为GCN只是简单地学习欺诈节点之间的相似性，以识别相似尝试的集群，并且需要更小的数据集来学习。我们展示了我们的结果与强链接和弱链接的关联账户，以确定欺诈团伙具有很高的信心。我们的结果优于标签传播社区发现和监督GBT算法的解决方案的质量和计算时间。
英文摘要：
In this paper, we present a novel approach to identify linked fraudulent activities or actors sharing similar attributes, using Graph Convolution Network (GCN). These linked fraudulent activities can be visualized as graphs with abstract concepts like relationships and interactions, which makes GCNs an ideal solution to identify the graph edges which serve as links between fraudulent nodes. Traditional approaches like community detection require strong links between fraudulent attempts like shared attributes to find communities and the supervised solutions require large amount of training data which may not be available in fraud scenarios and work best to provide binary separation between fraudulent and non fraudulent activities. Our approach overcomes the drawbacks of traditional methods as GCNs simply learn similarities between fraudulent nodes to identify clusters of similar attempts and require much smaller dataset to learn. We demonstrate our results on linked accounts with both strong and weak links to identify fraud rings with high confidence. Our results outperform label propagation community detection and supervised GBTs algorithms in terms of solution quality and computation time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
