graph-embedding

Graph embedding converts nodes, edges, or entire subgraphs into a low-dimensional vector representation, allowing for the application of machine learning algorithms that require vector inputs. This method preserves the topological and structural information of the graphs, enabling tasks such as link prediction and node classification. A misconception is that graph embedding is only useful for social network analysis; it has applications in domains like biology and recommendation systems as well.