Paper proposes neural networks for learning functions from sets to graphs.
problem Challenges in learning Set2Graph functions, including computational and memory complexity.
method Develops a family of neural network models that are practical and of maximal expressive power, approximating arbitrary continuous Set2Graph functions.
result Models can approximate arbitrary continuous Set2Graph functions over compact sets.