Hey,
I am currently trying to get started using DGL, by building a model that determines the maximum independent set of a graph. Recent (and older) papers do this by classifying each node of a graph with a score in [0,1], and then explore the solution space from this assignment.
However, I already struggle with the dataset preparation. The DGL documentation states how to create a dataset for node classification and graph classification. However, the node classification example assumes there only is a single graph, which is not true for MIS prediction. In MIS, we want to find one MIS per graph, i.e., label all vertices of a graph, but we have to train on a lot of graphs to learn this. I donâ€™t understand how I should build a DGLDataset that contains multiple graphs, but labels per vertex on a graph, not per graph. Things I am not sure about include:

how is the index in
__getitem__
mapped to a vertex/graph? Do we return all labels for all vertices of graph idx? How should the Dataset class be structured in general? 
How does batching work in this case? Because we will have to calculate the loss using all vertex labels of a single graph, but then somehow deal with multiple losses.
Any help/pointers are very much appreciated. Thank you ver ymuch.
Best,
Maximilian