Summary
In this chapter, we learned about Node2Vec, a second architecture based on the popular Word2Vec. We implemented functions to generate biased random walks and explained the connection between their parameters and two network properties: homophily and structural equivalence. We showed their usefulness by comparing Node2Vec’s results to DeepWalk’s for Zachary’s Karate Club. Finally, we built our first RecSys using a custom graph dataset and another implementation of Node2Vec. It gave us correct recommendations that we will improve even more in later chapters.
In Chapter 5, Including Node Features with Vanilla Neural Networks, we will talk about one overlooked issue concerning DeepWalk and Node2Vec: the lack of proper node features. We will try to address this problem using traditional neural networks, which cannot understand the network topology. This dilemma is important to understand before we finally introduce the answer: graph neural networks.
