# Link Prediction using Graph Neural Networks for Master Data Management

arXiv: https://arxiv.org/abs/2003.04732 · HTML: https://mlsanity.com/paper/2003.04732/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2020.

## Built on by (most cited first, 8 of 8)

- [Similar Cases Recommendation using Legal Knowledge Graphs](https://mlsanity.com/paper/2107.04771/) (2021)
- [Towards Automated Evaluation of Explanations in Graph Neural Networks](https://mlsanity.com/paper/2106.11864/) (2021)
- [Document Structure aware Relational Graph Convolutional Networks for Ontology Population](https://mlsanity.com/paper/2104.12950/) (2021)
- [Reimagining GNN Explanations with ideas from Tabular Data](https://mlsanity.com/paper/2106.12665/) (2021)
- [xLP: Explainable Link Prediction for Master Data Management](https://mlsanity.com/paper/2403.09806/) (2024)
- [xEM: Explainable Entity Matching in Customer 360](https://mlsanity.com/paper/2212.00342/) (2022)
- [A Tight Expressivity Hierarchy for GNN-Based Entity Resolution in Master Data Management](https://mlsanity.com/paper/2603.27154/) (2026)
- [Corporate-Family Resolution Is Not a String-Matching Problem: A Public Benchmark Stratified by Name Visibility](https://mlsanity.com/paper/2609.04269/) (2026)

## Abstract (arXiv)

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.