DGL: Billion-Scale Graphs and Sparse Matrix API

In a new release 0.9.1 DGL accelerated the pipeline of working with very large graphs (5B edges). Before it was taking 10 hours and 4TB of RAM and now 3 hours and 500GB of RAM, which also reduces the cost by 4x.

Also, if you use or would like to use sparse API for your GNNs, you can provide the feedback and use cases to the DGL team (feel free to reach out to @ivanovserg990 to connect). They are looking for the following profiles:

* Researchers/students who are familiar with sparse matrix notations or linear algebra.
* May have math or geometry backgrounds.
* Work majorly on innovating GNN architecture; less on domain applications.
* May have PyG/DGL experience.