Graph Convolutional Layers

Graph convolutional layers are a way for neural networks to analyze data that is represented as a graph, where entities (nodes) are connected by relationships (edges). These layers update each node’s information by combining its current data with that of its neighbors, effectively capturing the structure and context of the network. This process allows the model to learn patterns and features that depend on both the individual nodes and their connections. It's similar to how social influence spreads through a network, enabling the system to understand complex and interconnected information more effectively.