Graph representation learning is a subfield of machine learning that focuses on learning meaningful representations of nodes, edges, and entire graphs in complex networks. These representations, often in the form of embeddings or feature vectors, can be used for various tasks such as node classification, link prediction, and community detection. Graph representation learning techniques include network embedding methods, graph convolutional networks (GCNs), and graph attention networks (GATs), among others.