Graph Neural Networks (GNNs)

Graph Neural Networks (GNNs) are a class of deep learning models designed to process and analyze graph-structured data. GNNs leverage the inherent structural information of graphs to learn powerful node and graph representations, enabling them to capture complex dependencies and propagate information effectively across the graph

Here, we will explore the capabilities of GNNs and their applications in various machine-learning tasks.

Capabilities of GNNs

GNNs offer several advantages in handling various machine learning tasks, including:

• Node Classification: GNNs can accurately classify nodes in a graph based on their features and the relationships they have with other nodes.
• Link Prediction: GNNs can predict missing or future links in a graph, enabling them to model dynamic relationships and make accurate predictions.
• Graph Classification: GNNs can classify entire graphs based on their structural properties and the features of their nodes and edges.
• Community Detection: GNNs can identify communities or clusters of nodes with similar characteristics, helping to uncover hidden patterns and structures in complex networks.
• Recommendation Systems: GNNs can provide personalized recommendations by analyzing the…