4 Graph Algorithms on Steroids for data Scientists with cuGraph

We, as data scientists have gotten quite comfortable with Pandas or SQL or any other relational database.

We are used to seeing our users in rows with their attributes as columns. But does the real world behave like that?

In a connected world, users cannot be considered as independent entities. They have got certain relationships with each other, and we would sometimes like to include such relationships while building our machine learning models.

Now while in a relational database, we cannot use such relations between different rows(users), in a graph database, it is relatively trivial to do that.

Now, as we know, Python has a great package called Networkx to do this. But the problem with that is that it is not scalable.

A GPU can help solve our scalability problems with its many cores and parallelization. And that is where RAPIDS.ai CuGraph comes in.

The RAPIDS cuGraph library is a collection of graph analytics that process data found in GPU Dataframes — see cuDF . cuGraph aims to provide …