Hypergraphs are a generalization of graphs where edges (aka nets) are allowed to connect
more than two vertices. They have a similarly wide range of applications as graphs. This …

We develop a fast and high quality multilevel algorithm that directly partitions hypergraphs
into k balanced blocks–without the detour over recursive bipartitioning. In particular, our …

We develop a multilevel algorithm for hypergraph partitioning that contracts the vertices one
at a time. Using several caching and lazy-evaluation techniques during coarsening and …

We present PuLP, a parallel and memory-efficient graph partitioning method specifically
designed to partition low-diameter networks with skewed degree distributions. Graph …

We present an improved coarsening process for multilevel hypergraph partitioning that
incorporates global information about the community structure. Community detection is …

We investigate hypergraph partitioning-based methods for efficient parallelization of
communicating tasks. A good partitioning method should divide the load among the …

We present a refinement framework for multilevel hypergraph partitioning that uses max-flow
computations on pairs of blocks to improve the solution quality of ak-way partition. The …

We present an overview over our graph partitioners KaFFPa (Karl-sruhe Fast Flow
Partitioner) and KaFFPaE (KaFFPa Evolutionary). KaFFPa is a multilevel graph partitioning …

We propose a comprehensive and generic framework to minimize multiple and different
volume-based communication cost metrics for sparse matrix dense matrix multiplication …

Hypergraph partitioning has a wide range of applications such as VLSI design or scientific
computing. With focus on solution quality we develop the first multilevel memetic algorithm to …