Representation Learning on Graphs: Methods and Applications William L. Hamilton wleif@stanford.edu Rex Ying rexying@stanford.edu Jure Leskovec jure@cs.stanford.edu Department of Computer Science Stanford University Stanford, CA, 94305 Abstract Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks. The primary challenge in this domain is finding a way to represent, or encode, graph structure so that it can be easily exploited by machine learning models. Traditionally, machine learning approaches relied on user-defined heuristics to extract features encoding structural information about a graph (e.g., degree statistics or kernel functions). However, recent years have seen a surge in approaches that automatically learn to encode graph structure into low-dimensional embeddings, using techniques based on deep learning and nonlinear dimensionality reduction. Here we provide a
