Node2vec Application

Node2vec is a graph embedding technique that learns low-dimensional vector representations of nodes in a network, aiming to preserve both local and global network structure. Current research focuses on improving node2vec's scalability, particularly for dynamic graphs like blockchain transactions and evolving social networks, and enhancing its performance through techniques such as incorporating additional features (e.g., transaction history, student mastery levels), and integrating it with deep learning models for tasks like link prediction and fraud detection. These advancements are significant because effective graph embeddings enable improved performance in various applications, including anomaly detection, personalized learning systems, and action recognition from skeletal data.