Visualizing Graph Embeddings with t-SNE in Python

In my previous post we discussed the purpose and nature of graph embeddings. The main idea is that to do machine learning on a graph we need to convert the graph into a series of vectors (embeddings) that we can then use to train our machine learning (ML) models. The catch is that graph embeddings can be difficult to tune. Similar to other ways to create embeddings and the models they are used for, there are a lot of hyperparameters that we need to consider and optimizing them to the specific application takes time. The subject of tuning the embeddings is something I will save for a future post.