A Graph Neural Network (GNN) is a deep learning model designed to process data represented as graphs, which consist of nodes (entities) and edges (relationships). Unlike traditional neural networks that work with grid-like data (e.g., images) or sequences (e.g., text), GNNs handle irregular, non-Euclidean structures. This makes them suitable for tasks where relationships between entities are critical, such as social networks, molecular chemistry, or recommendation systems. GNNs learn by propagating information across the graph, updating node representations based on their neighbors, enabling the model to capture both local and global structural patterns.
GNNs operate through a mechanism called message passing. In each layer, nodes aggregate features from their neighbors, combine this information with their own, and update their embeddings (vector representations). For example, in a social network graph, a user node might aggregate data from friends to predict interests. Common architectures like Graph Convolutional Networks (GCNs) use weighted aggregation, while Graph Attention Networks (GATs) apply attention mechanisms to prioritize influential neighbors. Stacking multiple layers allows GNNs to capture higher-order relationships—like indirect connections two hops away. However, deep GNNs can face oversmoothing, where node features become too similar, so practical implementations often use shallow architectures or skip connections.
Developers apply GNNs to diverse problems. For instance, in recommendation systems, user-item interactions form a bipartite graph, and GNNs predict unobserved preferences by analyzing connectivity. In chemistry, molecules are modeled as graphs with atoms as nodes and bonds as edges; GNNs predict properties like toxicity. Frameworks like PyTorch Geometric and DGL simplify implementation with prebuilt layers for message passing and graph batching. A typical workflow involves defining the graph structure (adjacency matrix, node features), selecting a GNN variant, and training for tasks like node classification (e.g., labeling users as fraudsters) or link prediction (e.g., suggesting friendships). Challenges include scaling to large graphs (billions of nodes) and handling dynamic graphs where connections change over time.