Stanford CS224W: Machine Learning with Graphs - Stanford CS224W: ML with Graphs | 2021 | Lecture 3.2-Random Walk Approaches for Node Embeddings

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3jErMlt Jure Leskovec Computer Science, PhD In this video we look at a more effective similarity function - the probability of node co-occurrence in random walks on the graph. We introduce the intuition behind random walks, the objective function we will be optimizing, and how we can efficiently perform the optimization. We introduce node2vec, that combines BFS and DFS to generalize the concept of random walks. To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs224w/ 0:00 Introduction 0:12 Notation 3:27 Random-Walk Embeddings 4:34 Why Random Walks? 5:18 Unsupervised Feature Learning 6:07 Feature Learning as Optimization 7:12 Random Walk Optimization 11:07 Negative Sampling 13:37 Stochastic Gradient Descent 15:59 Random Walks: Summary 16:49 How should we randomly walk? 17:29 Overview of nodezvec 19:41 BFS vs. DFS 19:57 Interpolating BFS and DFS 20:52 Biased Random Walks 23:47 nodezvec algorithm 24:50 Other Random Walk Ideas 25:46 Summary so far