Unlocking the power of relational data through deep learning on graphs.
Data isn't just isolated points. In a graph, entities (Nodes) are defined by their connections (Edges) to others. GNNs leverage this structure directly.
The heart of a GNN. Nodes "talk" to their neighbors, exchanging information to update their internal state (embeddings) iteratively.
After learning node representations, the network aggregates them to make predictions at the node, edge, or entire graph level.
Predicting molecular properties by treating atoms as nodes and bonds as edges to find new life-saving compounds.
Detecting fake news, recommending friends, or analyzing community structures in massive social graphs.
Modeling road networks to forecast traffic flow and optimize routes in real-time navigation systems.