Graph Neural Network

Graph Neural Networks (GNNs) are a specialized class of neural networks specifically built to process and learn from data that is inherently structured as a graph. Unlike traditional neural networks that excel with grid-like data (like images) or sequences (like text), GNNs can understand relationships and dependencies between interconnected nodes and edges. They achieve this by iteratively aggregating information from a node's neighbors, effectively learning representations that capture both local and global graph structures.

This makes GNNs particularly powerful for tasks where relationships are key, rather than just individual data points. For example, in social networks, a GNN can predict friendships or suggest relevant content by analyzing how users are connected. In drug discovery, GNNs can model molecules as graphs to predict their properties or interactions by understanding the connections between atoms. They are also used in recommender systems, fraud detection, and traffic prediction, constantly learning how components in a system relate to one another.