Graph embeddings

Graph embeddings are techniques that convert the complex structure of a graph—comprising nodes and their connections—into simpler, continuous vector representations (like lists of numbers). These vectors capture the relationships and patterns within the graph, making it easier for algorithms to analyze, compare, or predict information such as node similarity or community structure. Essentially, graph embeddings translate the graph’s topology into a format that machine learning models can efficiently process, enabling tasks like recommendation systems, social network analysis, and fraud detection with improved accuracy and scalability.