Embeddings in graph neural networks (GNNs) are numerical representations of nodes, edges, or entire graphs that capture their structural and feature-based relationships. These embeddings allow GNNs to process graph data—which is inherently non-Euclidean—using standard machine learning techniques. The core idea is to map each node or edge to a dense vector in a continuous space, preserving properties like node connectivity, feature similarity, or community structure. For example, in a social network graph, embeddings might encode user interests and friendship patterns into vectors that a model can use for tasks like recommendation or classification.
GNNs generate embeddings through iterative message-passing steps, where nodes aggregate information from their neighbors. A common approach is to start with initial node features (e.g., user profiles in a social network) and iteratively update each node’s embedding by combining its current state with aggregated data from adjacent nodes. For instance, Graph Convolutional Networks (GCNs) apply a weighted average of neighboring node features, while GraphSAGE samples neighbors and uses aggregation functions like mean or LSTM. These methods ensure that embeddings reflect both local structure (direct connections) and broader graph topology. For example, in a citation network, a paper’s embedding might incorporate its own keywords and the topics of cited papers, enabling tasks like predicting research domains.
Embeddings are applied downstream in tasks like node classification, link prediction, or graph classification. In node classification, embeddings help predict labels (e.g., detecting fraudulent users in a transaction graph). For link prediction, embeddings of two nodes can be compared (e.g., using dot products) to estimate the likelihood of a connection. Frameworks like PyTorch Geometric or DGL simplify implementing these steps by providing built-in layers for message passing and aggregation. A practical example is recommendation systems: embeddings of users and items (represented as nodes) can be used to predict interactions, leveraging both user-item interactions and user-user/item-item similarities encoded in the graph structure.