Image by Cajeo Zhang Graphs have been used across many fields due to their ability to represent relationships between entities with applications including social networks, search engines, and protein-protein interaction networks. However, one growing limitation of these graphs are the amount of computational resources they require with some large-scale graphs having millions of nodes each with their own set of features and their set of edges. This has led to the creation of graph embedding methods, more specifically the deep embedding methods. These embedding methods aim to create a high-quality representation of the nodes and their edges. Rather than just incorporating the graph structural information into an embedding, these methods also include node and edges features and other hierarchical information. This results in a complicated model which are able to learn very rich representations of nodes.