Understanding GraphSAGE

Consider the graph below, which models a movie content platform. You have various users and a few movies and TV shows that users have watched. To keep things simple, there will be only four programs and three users. If you wanted to compare different movies to each other, you might want to consider embeddings. Embeddings can transform a graph structure into a vector of numbers, which can be used in downstream machine learning tasks. GraphSAGE is an inductive embedding model. That means you can generate embeddings for new nodes without having to retrain on your whole graph. It works in three (mostly) simple steps: GraphSAGE will multiply both a node’s own features and the features of its neighbors by learned weights to get a set of embeddings. Those embeddings will be compared to either a target value (in supervised mode) or to a connected node’s embeddings to measure error. Finally, using gradient descent, GraphSAGE will seek to find a set of weights that minimizes this error. First, you need to take a uniform sample of neighbors. You get to choose how many neighbors will be in your sample. In this case, our sample will consist of two neighbors for a…