Hands-On Graph Neural Networks Using Python

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Improving Embeddings with Biased Random Walks in Node2Vec

Node2Vec is an architecture largely based on DeepWalk. In the previous chapter, we saw the two main components of this architecture: random walks and Word2Vec. How can we improve the quality of our embeddings? Interestingly enough, not with more machine learning. Instead, Node2Vec brings critical modifications to the way random walks themselves are generated.

In this chapter, we will talk about these modifications and how to find the best parameters for a given graph. We will implement the Node2Vec architecture and compare it to using DeepWalk on Zachary’s Karate Club. This will give you a good understanding of the differences between the two architectures. Finally, we will use this ...