node2vec is a framework for learning continuous representations of nodes in a graph, capturing both local and global structures. It uses a biased random walk approach to explore the graph, allowing for flexible encoding of node features which can be useful in various tasks like link prediction and node classification. By transforming nodes into vector spaces, node2vec facilitates the use of machine learning algorithms on graph data.
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