Graph neural networks are machine learning models designed to work directly with graph structured data. They treat objects as nodes and their relationships as edges, capturing both local neighborhoods and long range connectivity. This makes them well suited to domains where relationships matter as much as the individual entities.

Their central mechanism is message passing. Each node aggregates information from its neighbors, transforms it through a neural network layer, and updates its own representation. Stacking multiple layers lets information flow across larger portions of the graph. Variants differ in how they aggregate neighbor information, how they weight edges, and how they handle directionality or edge features.

These models are powerful for semi supervised learning. With labels on only a subset of nodes, graph neural networks diffuse label information through the network structure. This has enabled strong performance in citation networks, social graphs and knowledge graphs. They also support link prediction and graph level tasks such as classifying entire molecules or materials.

A key challenge is scalability. Large graphs with millions of nodes and edges require efficient sampling and training strategies. Recent work explores neighborhood sampling, subgraph training and distributed implementations. Another line of research studies over smoothing, where deep stacks of graph layers cause node representations to become indistinguishable, and proposes architectural changes or normalization schemes to mitigate this.

The field is increasingly application driven. In chemistry and materials science, graph neural networks model atoms and bonds to predict properties. In recommendation systems, they capture user item interaction graphs. In physics inspired settings, they approximate dynamical systems while respecting underlying relational structure.