In recent years, significant advances have been made in the design and evaluation of
balanced (hyper) graph partitioning algorithms. We survey trends of the past decade in …

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 present an unsupervised technique for visual learning which is based on density
estimation in high-dimensional spaces using an eigenspace decomposition. Two types of …

We investigate an efficient parallelization of the most common iterative sparse tensor
decomposition algorithms on distributed memory systems. A key operation in each iteration …

We consider two-dimensional partitioning of general sparse matrices for parallel sparse
matrix-vector multiply operation. We present three hypergraph-partitioning-based methods …

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 …

K-way hypergraph partitioning has an ever-growing use in parallelization of scientific
computing applications. We claim that hypergraph partitioning with multiple constraints 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 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 …