CS224W: Machine Learning with Graphs | 2021 | Lecture 4.1 - PageRank

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: Jure Leskovec Computer Science, PhD In this lecture we focus on how to represent graphs as matrices and discuss subsequent properties that we can explore. We define the notion of PageRank, further explore Random Walks, and introduce Matrix Factorization as a perspective for generating node embeddings. For the first part of the lecture, we introduce PageRank as a method for ranking node importance within a graph. In doing so we present a matrix formulation of PageRank and show the connection to solving for the stationary distribution of a random walk over the graph. To follow along with the course schedule and syllabus, visit: