  Graph neural networks (GNNs) have found application for learning in the space
of algorithms. However, the algorithms chosen by existing research (sorting,
Breadth-First search, shortest path finding, etc.) usually align perfectly with
a standard GNN architecture. This report describes how neural execution is
applied to a complex algorithm, such as finding maximum bipartite matching by
reducing it to a flow problem and using Ford-Fulkerson to find the maximum
flow. This is achieved via neural execution based only on features generated
from a single GNN. The evaluation shows strongly generalising results with the
network achieving optimal matching almost 100% of the time.
