Behind the scenes on the Fast Random Projection algorithm for generating graph embeddings

The vast majority of data science and machine learning models rely on creating a vector, or embedding, of your data. Some of these embeddings naturally create themselves. For example, for numerical data organized in columns we can think of the values associated with each row as a single vector. In more complicated cases such as natural language processing we have to generate those embeddings from the words through a variety of different approaches like one-hot encoding, skip-gram methods such as word2vec, etc. These vectors are then used as the representation of the data that is to be modeled.