Node2vec is an algorithmic framework for learning continuous feature representations of nodes in networks. It aims to maximize the likelihood of preserving network neighborhoods of nodes, allowing for a flexible exploration of diverse neighborhoods. The algorithm uses a biased random walk procedure to efficiently sample neighborhoods, which can be tuned to balance between breadth-first (BFS) and depth-first (DFS) exploration. This flexibility enables Node2vec to capture both homophily (node similarity within communities) and structural equivalence (node similarity based on roles or roles in the network). The paper demonstrates the effectiveness of Node2vec through experiments on multi-label classification and link prediction tasks in various real-world networks. Node2vec outperforms state-of-the-art techniques, achieving up to 26.7% improvement in multi-label classification and 12.6% in link prediction. The algorithm is also shown to be robust to perturbations and scalable to large networks, making it a powerful tool for network analysis and representation learning.Node2vec is an algorithmic framework for learning continuous feature representations of nodes in networks. It aims to maximize the likelihood of preserving network neighborhoods of nodes, allowing for a flexible exploration of diverse neighborhoods. The algorithm uses a biased random walk procedure to efficiently sample neighborhoods, which can be tuned to balance between breadth-first (BFS) and depth-first (DFS) exploration. This flexibility enables Node2vec to capture both homophily (node similarity within communities) and structural equivalence (node similarity based on roles or roles in the network). The paper demonstrates the effectiveness of Node2vec through experiments on multi-label classification and link prediction tasks in various real-world networks. Node2vec outperforms state-of-the-art techniques, achieving up to 26.7% improvement in multi-label classification and 12.6% in link prediction. The algorithm is also shown to be robust to perturbations and scalable to large networks, making it a powerful tool for network analysis and representation learning.