Covers: theory of Graph Embeddings

- How can "deep walk" and "node2vec" be used to obtain graph embeddings?

This video contains:

- What are Node Embeddings
- Overview of DeepWalk
- Overview of Node2vec

Fail to play? Open the link directly: https://youtu.be/oDsbCoP_9Ac

Karim Khayrat

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Contributors

- Objectives
- This recipe contains a few resources for the task of predicting a missing propery of a node in graph data
- Potential Use Cases
- outlier detection in network
- Who is This For ?
- INTERMEDIATEdata scientists

Click on each of the following **annotated items** to see details.

Resource Asset5/7

VIDEO 1. Simple Graph Convolution

- What is a simple way to use convolution to get graph embeddings?

8 minutes

VIDEO 2. Graph Attention Networks

- How can attention be used to obtain graph embeddings?

5 minutes

REPO 3. Node Classification using Graph Attention Networks

- How can GAT be used to classify node properties?

20 minutes

OTHER 4. Neo4J Node Properties

- What are some possible node properties of interest?

10 minutes

REPO 5. Node Classification using Simple Graph Convolution

- How can SGC be used to predict node properties?

20 minutes

VIDEO 6. Deep Walk and Node2Vec

- How can "deep walk" and "node2vec" be used to obtain graph embeddings?

10 minutes

RECIPE 7. Node Classification using Graph Neural Networks

10 minutes

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