A Graph Neural Network (GNN) is a deep learning architecture designed to process and learn from data represented as graphs - structures consisting of nodes (vertices) connected by edges. Unlike traditional neural networks that work with regular grid-like data (such as images) or sequential data (such as text), GNNs can handle irregular data structures where relationships between elements are key to understanding the underlying patterns.
A prediction task with a GNN model
At their core, GNNs operate through an iterative process of message passing and node updating. During message passing, each node in the graph gathers information from its neighboring nodes and the edges that connect them. The nodes then update their representations based on this gathered information, allowing the network to learn increasingly sophisticated features that capture both local and global graph structure. This process enables GNNs to learn rich representations that preserve the relational information inherent in graph-structured data.
GNNs have found widespread applications across diverse fields, from drug discovery and molecular property prediction to social network analysis and traffic forecasting. In drug discovery, GNNs can model molecules as graphs where atoms are nodes and chemical bonds are edges, enabling the prediction of chemical properties and potential interactions. In social networks, GNNs can identify communities and influential users by analyzing the patterns of connections between users. Their ability to model complex relationships has also made them valuable tools in recommendation systems, where they can capture nuanced user-item interactions, and in knowledge graphs, where they can reason about relationships between different entities.
To learn more about GNNs, check out this amazing interactive explainer by Google researchers Benjamin Sanchez-Lengeling, Emily Reif, Adam Pearce and Alexander B. Wiltschk.
