  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.
