.png){kind=icon}
返回
Inductive Graph Representation Learning for fraud detection
.png){kind=icon}
OA
AI
DOI：10.1016/j.eswa.2021.116463.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Graphs can be seen as a universal language to describe and model a diverse set of complex systems and data structures. However, efficiently extracting topological information from dynamic graphs is not a straightforward task. Previous works have explored a variety of inductive graph representation learning frameworks, but despite the surge in development, little research deployed these techniques for real-life applications. Most earlier studies are restricted to a set of benchmark experiments, rendering their practical generalisability questionable. This paper evaluates the proclaimed predictive performance of state-of-the-art inductive graph representation learning algorithms on highly imbalanced credit card transaction networks. More specifically, we assess the inductive capability of GraphSAGE and Fast Inductive Graph Representation Learning in a fraud detection setting. Credit card transaction fraud networks pose two crucial challenges for graph representation learners: First, these networks are highly dynamic, continuously encountering new transactions. Second, they are heavily imbalanced, with only a small fraction of transactions labelled as fraudulent. This paper contributes to the literature by (i) proving how inductive graph representation learning techniques can be leveraged to enhance predictive performance for fraud detection and (ii) demonstrating the benefit of graph-level undersampling for representation learning in imbalanced networks.
Keyword:
Inductive Representation Learning
Graph embeddings
Class imbalance
Fraud detection
GraphSAGE
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
机构
引用论文
Insurance activity and economic performance: Fresh evidence from asymmetric panel causality testsOAAI
.png){kind=icon}
OAAI分享收藏APATE: A novel approach for automated credit card transaction fraud detection using network-based extensionsAPATE: 一种使用基于网络的扩展进行自动信用卡交易欺诈检测的新方法OAAI
OAAI分享收藏Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy信用卡欺诈检测: 现实的建模和新颖的学习策略OAAI
OAAI分享收藏分享收藏分享收藏
查看更多内容