Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 3.3 - Embedding Entire Graphs
52.9 هزار بار بازدید -
4 سال پیش
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Jure Leskovec
Computer Science, PhD
In some scenarios it is important to not only learn embeddings for nodes, but also the entire graph. In this video, we introduce several approaches that could effectively learn embeddings for entire graphs, including aggregation of node embeddings, as well as the anonymous walk embedding approach.
To follow along with the course schedule and syllabus, visit:
http://web.stanford.edu/class/cs224w/
0:00 Introduction
0:27 Embedding Entire Graphs
0:58 Approach 2
2:54 Approach 3: Anonymous Walk Embeddings
5:02 Number of Walks Grows
5:52 Simple Use of Anonymous Walks
7:09 Sampling Anonymous Walks
8:27 New idea: Learn Walk Embeddings
13:59 Preview: Hierarchical Embeddings
14:32 How to Use Embeddings
16:21 Today's Summary
Jure Leskovec
Computer Science, PhD
In some scenarios it is important to not only learn embeddings for nodes, but also the entire graph. In this video, we introduce several approaches that could effectively learn embeddings for entire graphs, including aggregation of node embeddings, as well as the anonymous walk embedding approach.
To follow along with the course schedule and syllabus, visit:
http://web.stanford.edu/class/cs224w/
0:00 Introduction
0:27 Embedding Entire Graphs
0:58 Approach 2
2:54 Approach 3: Anonymous Walk Embeddings
5:02 Number of Walks Grows
5:52 Simple Use of Anonymous Walks
7:09 Sampling Anonymous Walks
8:27 New idea: Learn Walk Embeddings
13:59 Preview: Hierarchical Embeddings
14:32 How to Use Embeddings
16:21 Today's Summary
4 سال پیش
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