Watch a real-world coding example of official DGL on a Knowledge Graph for medical research. Understand in real-time why a Graph Neural Network is so important to gain insight in complex data sets, highlighting a heterogeneous Knowledge Graph in DGL code. All Credits to: A team of AWS scientists from Amazon Shanghai AI Lab and AWS Deep Engine Science team working along with academic collaborators from University of Minnesota, Ohio State University, and Hunan University have created the Drug Repurposing Knowledge Graph (DRKG) and a set of machine learning tools that can be used to prioritize drugs for re-purposing studies. These institutions provide the code and verbal description in the officially published DGL Blog, which I just follow and implement in real-time coding in AWS SageMaker Studio Lab. Link to original published Blog content by DGL-KE in DGL: https://www.dgl.ai/news/2020/06/09/covid.html #KnowledgeGraph #DGLKE #NetworkX 00:00 DGL Blog example 02:42 SageMaker Studio JupyterLab 07:00 Node entities 09:00 Edge types 09:30 NetworkX 13:12 Training on DGL-KE 14:55 Entity Embedding 16:45 Low-dim Relations embedding 17:45 Summary