Graph Embedding

Graph embedding represents nodes, edges, or entire graphs as vectors so that useful relationships—such as proximity, structural role, hierarchy, or direction—can be analyzed and used in downstream tasks. Common approaches include random walks and matrix factorization, spectral methods, graph neural networks and autoencoders, and geometric models that use spaces such as hyperbolic or asymmetric metrics. These representations support tasks including link prediction, node classification, clustering, graph retrieval, and analysis of knowledge, social, citation, and dynamic networks. A central challenge is balancing task-specific accuracy with robustness, interpretability, and scalability, since performance and stability can vary substantially with graph structure and embedding choices.