Graph neural networks (GNNs) are specialized neural networks tailored for processing data organized in graph structures, where nodes symbolize data points and edges signify relationships between them. Utilizing a message-passing mechanism, GNNs aggregate information from neighboring nodes, enabling the extraction of intricate patterns and relationships within the graph. This capability makes GNNs particularly valuable for applications such as social network analysis, molecular structure prediction, and recommendation systems, where understanding complex interconnections is crucial.
