Graph Convolutional Network

TR: Grafik Evrişim Ağı

In One Line

A foundational GNN architecture that learns representations over graphs by using neighborhood information.

A Graph Convolutional Network adapts the idea of convolution from images to graph structure through node-neighborhood relationships. Each node learns a richer representation by aggregating information from its neighbors. It has important applications in social networks, molecular structures, knowledge graphs, and recommendation systems. Its core strength is that it directly incorporates relational structure into the learning process.