  De novo genome assembly focuses on finding connections between a vast amount
of short sequences in order to reconstruct the original genome. The central
problem of genome assembly could be described as finding a Hamiltonian path
through a large directed graph with a constraint that an unknown number of
nodes and edges should be avoided. However, due to local structures in the
graph and biological features, the problem can be reduced to graph
simplification, which includes removal of redundant information. Motivated by
recent advancements in graph representation learning and neural execution of
algorithms, in this work we train the MPNN model with max-aggregator to execute
several algorithms for graph simplification. We show that the algorithms were
learned successfully and can be scaled to graphs of sizes up to 20 times larger
than the ones used in training. We also test on graphs obtained from real-world
genomic data---that of a lambda phage and E. coli.
