/tutorials/models/README.txt

Graph-based Neural Network Models
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We developed DGL with a broad range of applications in mind. Building
state-of-art models forces us to think hard on the most common and useful APIs,
learn the hard lessons, and push the system design.

We have prototyped altogether 10 different models, all of them are ready to run
out-of-box and some of them are very new graph-based algorithms. In most of the
cases, they demonstrate the performance, flexibility, and expressiveness of
DGL. For where we still fall in short, these exercises point to future
directions.

We categorize the models below, providing links to the original code and
tutorial when appropriate. As will become apparent, these models stress the use
of different DGL APIs.