DeepWalk and Node2Vec

DeepWalk and Node2Vec

This tutorial discusses two node (and edge) embedding methods: DeepWalk and node2vec (DeepWalk and node2vec)
We present the idea of using language models and adapt them to the graph setting by means of random walks sampling. In particular, we discuss the similarity and difference of the two approaches, by highlighting the fundamental ideas introduced by DeepWalk, and the generalizations provided by node2vec.
A second tutorial (next week) will present the computational details of the methods (i.e., Hierarchical Softmax and Negative Sampling), and discuss the implementation of the methods in PyTorch.

Download the material of the lecture here.