Graph Neural Networks Explained: A Clear Guide to GNN Basics & Models

A structured introduction to Graph Neural Networks (GNNs), covering the fundamentals of graph representation (nodes, edges, adjacency matrices, embeddings) and the message passing mechanism. Five major GNN architectures are explained: Graph Convolutional Networks (GCN), GraphSAGE, Graph Attention Networks (GAT), Graph Isomorphism Networks (GIN), and Graph Transformers. Each architecture's aggregation strategy is compared — GCNs smooth neighborhoods, GraphSAGE samples large graphs, GATs assign learned attention weights, GINs maximize structural expressivity via the WL test, and Graph Transformers enable global attention across all nodes.