Graph Neural Networks: GCN, MPNN, and GAT, Explained Simply

Graph neural networks (GNNs) apply neural network principles to graph-structured data, enabling them to leverage the relationships between entities for tasks like classification. They function by iteratively updating a node's feature representation by aggregating information from its neighbors, a process analogous to convolutions in image processing. A common type, the Graph Convolutional Network (GCN), uses an update rule based on the graph's adjacency matrix and node features, which is refined with normalization techniques to stabilize training. GNNs are trained via backpropagation and can generalize to new graph structures, making them powerful for node, edge, or graph-level classification tasks.