Link Prediction using Graph Neural Networks for Master Data Management
arXiv:2003.04732
Abstract
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.
arXiv comment10 pages, 11 figures
References in corpus (7)
- PyTorch: An Imperative Style, High-Performance Deep Learning Library
- Fast Graph Representation Learning with PyTorch Geometric
- Predicting multicellular function through multi-layer tissue networks
- AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias
- Neural Additive Models: Interpretable Machine Learning with Neural Nets
- Temporal Graph Networks for Deep Learning on Dynamic Graphs
- Fine Grained Classification of Personal Data Entities