Spatial Graph Convolutional Networks

Spatial Graph Convolutional Networks (GCNs) are a type of neural network designed to analyze data represented as graphs, where entities are nodes connected by edges. They work by aggregating information from each node’s neighbors directly in the graph space, allowing the model to learn patterns based on how nodes relate in the network. This approach is useful for data like social networks, molecules, or 3D structures, where relationships matter. Essentially, spatial GCNs enable computers to understand complex interconnected data by focusing on local neighborhoods, helping with tasks like node classification, link prediction, and graph understanding.