Graph Convolutional Networks

Graph Convolutional Networks (GCNs) are a type of neural network designed to analyze data represented as graphs, where entities are nodes connected by relationships (edges). They work by iteratively aggregating information from a node’s neighbors, allowing the network to learn patterns based on both a node’s features and its connections. This enables GCNs to effectively interpret complex, relational data such as social networks, molecules, or transportation systems, capturing contextual information that traditional models might miss. In essence, GCNs transform and analyze interconnected data to reveal underlying structures and insights.