Graph representation learning is a research field focused on developing methods to automatically learn low-dimensional vector representations (embeddings) for nodes, edges, or entire graphs. These methods aim to capture and preserve the structural, topological, and feature information of graphs, enabling the application of standard machine learning algorithms to graph-structured data for tasks such as node classification, link prediction, and graph classification.