This paper studies asynchronous message passing (AMP), a new paradigm for applying neural network based learning to graphs.
In DropGNNs, we execute multiple runs of a GNN on the input graph, with some of the nodes randomly and independently dropped in each of these runs.
Ranked #8 on Graph Classification on IMDb-B
Given there are quadratically many possible edges in a graph and each subset of edges is a possible solution, this yields unfeasibly large search spaces even for few nodes.
The automatic generation of medleys, i. e., musical pieces formed by different songs concatenated via smooth transitions, is not well studied in the current literature.