Graph Representation Learning is a technique used to convert graph data into a format that machine learning algorithms can understand. Graphs consist of nodes (or vertices) and edges that represent relationships between these nodes. By learning representations of these graphs, we can capture important structural and relational information, which can be useful for various tasks like node classification, link prediction, and graph classification. This approach often employs methods such as Graph Neural Networks (GNNs) or node embeddings to create low-dimensional representations of nodes or entire graphs. These representations help in identifying patterns and making predictions based on the graph's structure, enabling applications in fields like social network analysis, recommendation systems, and bioinformatics.