  We present a refinement framework for multilevel hypergraph partitioning that
uses max-flow computations on pairs of blocks to improve the solution quality
of a $k$-way partition. The framework generalizes the flow-based improvement
algorithm of KaFFPa from graphs to hypergraphs and is integrated into the
hypergraph partitioner KaHyPar. By reducing the size of hypergraph flow
networks, improving the flow model used in KaFFPa, and developing techniques to
improve the running time of our algorithm, we obtain a partitioner that
computes the best solutions for a wide range of benchmark hypergraphs from
different application areas while still having a running time comparable to
that of hMetis.
