论文标题
使用图形神经网络进行主数据管理的链接预测
Link Prediction using Graph Neural Networks for Master Data Management
论文作者
论文摘要
N- ARY关系数据的学习图表具有许多现实世界应用，例如反洗钱，欺诈检测和客户尽职调查。 Covid19积极人员的接触跟踪也可以作为链接预测问题。与到目前为止通常应用GNN相比，使用图神经网络之间的人之间的联系需要仔细的道德和隐私考虑。我们介绍了用于匿名数据，模型培训，解释性和验证链接预测的新颖方法，并讨论了我们的结果。
Learning graph representations of n-ary relational data has a number of real world applications like anti-money laundering, fraud detection, and customer due diligence. Contact tracing of COVID19 positive persons could also be posed as a Link Prediction problem. Predicting links between people using Graph Neural Networks requires careful ethical and privacy considerations than in domains where GNNs have typically been applied so far. We introduce novel methods for anonymizing data, model training, explainability and verification for Link Prediction in Master Data Management, and discuss our results.
