Graph Neural Network

Graph neural networks (GNNs) are a class of deep learning architectures that operate directly on graph-structured data — networks of nodes and edges — by propagating information between connected elements. Unlike standard neural networks, which assume a fixed grid or sequence structure, GNNs learn representations that respect relational topology, making them natural tools for molecular prediction, social network analysis, and reasoning over knowledge graphs. The core mechanism is iterative message passing: each node aggregates information from its neighbors, updates its own state, and passes the result onward, a process that can be understood as a differentiable approximation of belief propagation.

GNNs occupy a promising middle ground in the neural-symbolic integration space: they handle relational structure natively without requiring hand-coded symbolic rules, yet they struggle with systematic generalization to novel graph sizes and structures.