Node2Vec is a machine learning algorithm designed to learn continuous vector representations of nodes in a graph. It captures the structural information of the graph by simulating random walks, allowing the model to explore the local and global neighborhoods of each node. This helps in preserving the relationships between nodes in the resulting vector space. The algorithm uses two key parameters, p and q, to control the random walk behavior. By adjusting these parameters, Node2Vec can focus on either breadth-first or depth-first exploration, enabling it to generate diverse embeddings that can be used for various tasks, such as node classification and link prediction.