Graph Neural Networks (GNNs) are specialized deep learning models designed to process graph-structured data, capturing relationships between entities for tasks like node classification and link prediction.
GNNs are utilized in various domains, including social network analysis, fraud detection, recommendation systems, and biological data modeling, effectively handling complex, interconnected data structures.
By leveraging the structure of graphs, GNNs can model dependencies between nodes, leading to more accurate predictions and insights in systems where relationships are crucial.
Ongoing research aims to enhance GNN scalability, interpretability, and integration with other AI models, expanding their applicability across emerging fields and complex problem-solving scenarios.