Summary
In this chapter, we learned about DeepWalk architecture and its major components. Then, we transformed graph data into sequences using random walks to apply the powerful Word2Vec algorithm. The resulting embeddings can be used to find similarities between nodes or as input to other algorithms. In particular, we solved a node classification problem using a supervised approach.
In Chapter 4, Improving Embeddings with Biased Random Walks in Node2Vec, we will introduce a second algorithm based on Word2Vec. The difference with DeepWalk is that the random walks can be biased towards more or less exploration, which directly impacts the embeddings that are produced. We will implement this algorithm on a new example and compare its representations with those obtained using DeepWalk.
