Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 1.3 - Choice of Graph Representation

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For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: Jure Leskovec Computer Science, PhD One essential task to consider before we conduct machine learning on graphs is to find an appropriate way to represent the graphs. What are the factors that will affect our choices as to the representations? In this video, we’ll be looking at the different approaches to abstracting graphs: directed vs. undirected, weighted vs. unweighted, homogeneous vs bipartite, and so on. To follow along with the course schedule and syllabus, visit:

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