Covers: theory of Deep Walk

- What is Deep Walk?
- How does it utilize the ideas of NLP's word2vec?
- What representation learning for graph it can be used for?

Easy-to-understand paper walk through of the deep walk algorithm.

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Contributors

- Objectives
- Learn about the Node2Vec method to generate node embedding for downstream tasks.
- Potential Use Cases
- Perfect for when you want to use Graph Neural Network for Node Classification, Community Detection or Link Prediction, but have no node features (just edges).
- Who is This For ?
- INTERMEDIATEIntermediate ML developers with some knowledge of NLP and Graph Theory

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

ARTICLE 1. (optional) A word2vec introduction if you don't know it

- What is word2vec?
- How does it work and what it can do?

30 minutes

VIDEO 2. A refresher of the CBOW and Skipgram algorithm

- How does the Word2Vec algorithm work?

20 minutes

VIDEO 3. Explanation of Random Walk

- What is random walk?
- How does random walk work programmatically?

10 minutes

VIDEO 4. Deep Walk brings NLP techniques to Graph Theory

- What is Deep Walk?
- How does it utilize the ideas of NLP's word2vec?
- What representation learning for graph it can be used for?

15 minutes

VIDEO 5. Paper Walkthrough and Explanation of Node2Vec

- How biased random walk is used for sampling of node sequence?
- How node sequences are used to generate embeddings for nodes?
- How Node2Vec works?

15 minutes

PAPER 6. Using hyper-parameter p & q to influence walks

- How Node2Vec parameters are used to influence embedding results?
- What are the use cases proposed by the author?

15 minutes

ARTICLE 7. A walkthrough of how to implement Node2Vec for feature learning and downstream task

- How to implement Node2Vec on a downstream task?
- How to evaluate Node2Vec compared to a simple Deep Walk?

25 minutes

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