Link Prediction using Graph Neural Networks for Master Data Management
This addresses privacy and ethical challenges in applying GNNs to sensitive domains like anti-money laundering and contact tracing, but appears incremental as it builds on existing GNN techniques.
The paper tackled link prediction in master data management using graph neural networks, introducing novel methods for anonymization, training, explainability, and verification, and discussed the results.
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.