GEM provides a general framework for graph embedding methods that represent graphs in low-dimensional vector spaces. The library implements multiple embedding techniques—Locally Linear Embedding, Laplacian Eigenmaps, Graph Factorization, HOPE, SDNE, and node2vec—and includes utilities to evaluate embedding quality through graph reconstruction, link prediction, and visualization tasks.