Unlocking the Potential of Message Passing: Exploring GraphSAGE, GCN and GAT | GNN GraphML

Introduction to GRAPH ML, Graph Neural Networks (GNN) and the main idea behind Message Passing in graph network configurations of GraphSAGE, GCN and GAT. Message passing applied to Graph Convolutional Networks (GCN), GraphSAGE and Graph Attention Networks. The key difference between GAT and GCN is how the information from the k-hop neighborhood is aggregated. Stanford online: CS224W https://www.youtube.com/watch?v=JAB_plj2rbA&list=PLoROMvodv4rPLKxIpqhjhPgdQy7imNkDn #ai #graphs #theory