Graph embedding techniques represent graph-structured data in a continuous vector space while preserving the graph’s structural properties. Methods such as DeepWalk, Node2Vec, Graph Convolutional Networks (GCNs), and GraphSAGE enable efficient processing and analysis of graph data. These techniques are particularly valuable for tasks like node classification, link prediction, and graph clustering. Data scientists and researchers focusing on complex network analysis and machine learning applications can leverage graph embeddings to extract meaningful insights and improve model performance in graph-related tasks.
