Graph Embeddings (node2vec) explained - How nodes get mapped to vectors

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Learn how the node2vec algorithm works. 🚀

To unlock Machine Learning Algorithms on graphs, we need a way to represent our data networks as vectors.

I will help you understand how simple the node2vec approach is.

We will walk through the maths and explore how we can generate powerful graph embeddings.

1. Understand the problem
2. What's the context of a node in a graph
3. The equation of the optimization problem.
  1. Philipp Brunenberg
  2. Neo4j
  3. Graph Database
  4. Graph Embeddings
  5. Node2vec
  6. Data Science
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Комментарии
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Very good explanation. Thank you. I have one question if you don't mind. At time 6:48 you are showing the contexts of nodes u and v. However I am not sure how to interpret the contexts. Why are 4 and 8 left out of the context of u, and why are 3, 4 and 6 left out of the context of v?

farrugiamarc
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Pretty detailed and well-structured explanation, thanks!

AlexanderSergeenko
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Hi phillip,
Are we training on only positive contexts? Cant we show some negative contexts to model to make it more context aware

unknownfacts-mfhr
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Thank you for covering a not so popular topic. Much appreciated!

Till 6:35 I could follow along perfectly but afterwards, I was left confused. What is node v? Why are the context vectors different from both DFS and BFS from the previous slide? The orders are different and some numbers are missing.

prateekyadav
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Pretty Good! Philipp! Your explanation is easy to understand! I think it would be better if you can include the implementation of the technology with some popular coding techniques such as Python! Wait for more amazing videos!

zhiyuzhang
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Thank you for the explanation. Could you please provide the details of the paper you mentioned in the video?

suhailamsooppy
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Thank you for the lecture. Please guide on how to use these embeddings for Question _answer task using knowledge Graph

pranitamahajan
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Great content Phillip!, is it possible to create a more practical playlist for beginners?
Something we can follow

reubenseyram
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Thank you, I think that I get rough imagination of the paper.

linyaka