Peter BloemNow out in #TMLR:
đ GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks đ
There's lots of work on sampling subgraphs for GNNs, but relatively little on making this sampling process _adaptive_. That is, learning to select the data from the graph that is relevant for your task.
We introduce an RL-based and a GFLowNet-based sampler and show that the approach performs well on heterophilic graphs.
