Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs

no code implementations • 1 Dec 2018 • Daniel Neil, Joss Briody, Alix Lacoste, Aaron Sim, Paidi Creed, Amir Saffari

In this work, we provide a new formulation for Graph Convolutional Neural Networks (GCNNs) for link prediction on graph data that addresses common challenges for biomedical knowledge graphs (KGs).

Denoising Knowledge Graphs +1