What's Next After Learning Embeddings?

Graph analytics

Graph analytics is a branch of data analytics in which we deal with the pairwise relationships between different network entities. Different domains, including biological, physical, social, and telecommunicational, have the potential to benefit majorly from graph analytics. This is why there has been a surge in demand for graph data analysts.

Recently, we witnessed growing popular interest in cryptocurrencies and the different types of scams and frauds that arose as a result. Huge financial transaction graphs built using Bitcoin and other cryptocurrency transactions help fight money laundering. This is knowledge graph analytics.

Recommender systems can recommend a list of titles similar to your favorite movies, YouTube videos, and songs. This has been a part of our day-to-day life, with targeted ads no longer surprising us.

Optimizing airline routes, retail, inventory management, and electricity grids are some of the lesser-known examples in which graph analytics come in handy.

Predictive models

In the last chapter, we saw how to learn embeddings using different algorithms. The embeddings capture a graph’s local and global geometric properties, enabling us to perform various tasks. We can train predictive models using non-euclidean graphs as input with these embeddings.

So what can we predict using graphs, and how can we build predictive models from this complex data structure?

The following is a list of different formulations of graph-related tasks (also termed graph downstream tasks) and their machine learning task type: