Hypergraph Partitioning and Clustering

Papa, David A.; Markov, Igor L.

doi:10.1201/9781420010749.ch61

Cited by 58 publications

(50 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…each row is treated as a net and each column as a vertex. For SAT instances, each boolean variable (and its complement) is mapped to one vertex and each clause constitutes a net [4]. To evaluate the pin sparsifier and to compare our algorithm to other systems, we use the 294 hypergraphs that were also used in [19].…”

Section: Methodsmentioning

confidence: 99%

“…Instances. We evaluate our algorithm on a large collection of hypergraphs 4 , which contains instances from three benchmark sets: the ISPD98 VLSI Circuit Benchmark Suite [52], the University of Florida Sparse Matrix Collection [53], and the international SAT Competition 2014 [54]. All hypergraphs have unit vertex and net weights.…”

Section: Methodsmentioning

confidence: 99%

“…We refer to [4,[29][30][31] for an extensive overview, and focus instead on issues closely related to the contributions of our paper. The two most widely used generalpurpose tools are PaToH [3] (originating from scientific computing) and hMetis [15,32] (originating from VLSI design).…”

Section: Related Workmentioning

confidence: 99%

“…Email: {yaroslav.akhremtsev, sanders, sebastian.schlag}@kit.edu, tobias.heuer@gmx.net Hypergraph partitioning (HGP) has a wide range of applications. Two prominent areas are VLSI design and scientific computing (e. g. accelerating sparse matrixvector multiplications) [4]. While the former is an example of a field where small optimizations can lead to significant savings [5], the latter exemplifies problems where hypergraph-based modeling is more flexible than graph-based approaches [2,3,[6][7][8].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Engineering a direct k-way Hypergraph Partitioning Algorithm

Akhremtsev

Heuer

Sanders

et al. 2017

2017 Proceedings of the Ninteenth Workshop on Algorithm Engineering and Experiments (ALENEX)

View full text Add to dashboard Cite

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 algorithm efficiently implements the powerful FM local search heuristics for the complicated k-way case. This is important for objective functions which depend on the number of blocks connected by a hyperedge. We also remove several further bottlenecks in processing large hyperedges, develop a faster contraction algorithm, and a new adaptive stopping rule for local search. To further reduce the size of hyperedges, we develop a pin-sparsifier based on the min-hashing technique that clusters vertices with similar neighborhood. Extensive experiments indicate that our KaHyPar-partitioner compares favorably with the best previous systems. KaHyPar is faster than hMetis and computes better solutions. KaHyPar's results are considerably better than the (faster) PaToH partitioner.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Engineering a direct k-way Hypergraph Partitioning Algorithm

Akhremtsev

Heuer

Sanders

et al. 2017

2017 Proceedings of the Ninteenth Workshop on Algorithm Engineering and Experiments (ALENEX)

View full text Add to dashboard Cite

show abstract

“…The second stage of our algorithm, which computes the assignment solution for each robot, corresponds to an uncoarsening phase that refines the final results. As already pointed out, this multilevel partitioning strategy has been broadly used in many partitioning algorithms and much software, and the advantages of this framework are discussed by several authors [27,29,30].…”

Section: Assignment Partitioning and Distribution Algorithmmentioning

confidence: 99%

Multi-Level Partitioning and Distribution of the Assignment Problem for Large-Scale Multi-Robot Task Allocation

Liu

Shell

2011

Robotics: Science and Systems VII

View full text Add to dashboard Cite

Abstract-A team of robots can handle failures and dynamic tasks by repeatedly assigning functioning robots to tasks. This paper introduces an algorithm that scales to large numbers of robots and tasks by exploiting both task locality and sparsity. The algorithm mixes both centralized and decentralized approaches at different scales to produce a fast, robust method that is accurate and scalable, and reduces both the global communication and unnecessary repeated computation. We depart from optimization and bipartite matching formulations of the problem, observing instead that an assignment can be computed through coarsening and partitioning operations on the utility matrix. First, a coarse assignment is calculated by evaluating the global utility information and partitioning it into clusters in a problem-domain independent way. Next, the assignment solutions in each partition are refined (either recursively, or via an existing algorithm). This multilevel framework allows the repeated reassignment to execute among interrelated partitions. The results suggest that only a minor sacrifice in solution quality is required for gains in efficiency. The proposed algorithm is validated using extensive simulation experiments and the results show advantages over the traditional optimal assignment algorithms.

show abstract

(t,k)‐Hypergraph anonymization: an approach for secure data publishing

Asayesh

Hadavi

Jalili

2014

Security Comm Networks

View full text Add to dashboard Cite

Privacy preservation is an important issue in data publishing. Existing approaches on privacy-preserving data publishing rely on tabular anonymization techniques such as k-anonymity, which do not provide appropriate results for aggregate queries. The solutions based on graph anonymization have also been proposed for relational data to hide only bipartite relations. In this paper, we propose an approach for anonymizing multirelation constraints (ternary or more) with (t,k) hypergraph anonymization in data publishing. To this end, we model constraints as undirected hypergraphs and formally cluster attribute relations as hyperedge with the t-means-clustering algorithm. In addition, anonymization is carried out with a k-anonymity method in every cluster for which the parameter k can vary in each cluster, to attain more flexibility and less information loss with respect to utility. Our experiments demonstrate that this approach offers a great trade-off between privacy and utility.

show abstract

Hypergraph Partitioning and Clustering

Cited by 58 publications

References 39 publications

Engineering a direct k-way Hypergraph Partitioning Algorithm

Engineering a direct k-way Hypergraph Partitioning Algorithm

Multi-Level Partitioning and Distribution of the Assignment Problem for Large-Scale Multi-Robot Task Allocation

(t,k)‐Hypergraph anonymization: an approach for secure data publishing

Contact Info

Product

Resources

About