Graph neural networks 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.) are usually trivial, from the viewpoint of a theoretical
computer scientist. 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-Fulker-
son to find the maximum flow. This is achieved via neural execu-
tion 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.
