Stanford CS224W: ML with Graphs | 2021 | Lecture 2.2 - Traditional Feature-based Methods: Link

April 15, 2021
by
Stanford Online
YouTube video player
Stanford CS224W: ML with Graphs | 2021 | Lecture 2.2 - Traditional Feature-based Methods: Link

Transcript

Read and summarize the transcript of this video on Glasp Reader (beta).

Summary & Key Takeaways

The summary of this video is not yet available 😢 We summarize videos one by one, so it might take a while. If you want to get the summary now, please summarize the video with YouTube Summary extension or try Glasp Reader.

About the Video

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3mjkzZQ

Traditional Feature-based Methods: Link-level features

Jure Leskovec

Computer Science, PhD

In this video we introduce the important task of link prediction , as well as how to extract link-level features to better tackle such type of problems. This is useful in scenarios where we need to predict missing edges, or predict edges that will appear in the future. Link level features we’ll be talking about include distance-based ones, as well as local and global neighborhood overlap.

To follow along with the course schedule and syllabus, visit:

http://web.stanford.edu/class/cs224w/...

Share This Video 📚

Summarize YouTube Videos and Get Video Transcripts with 1-Click

Download browser extensions on:

Find Summaries from Stanford Online 📚