Papers hypergraph partitioning
“hypergraph partitioning” 태그가 달린 논문 26편 · 필터 해제
SHyPar: A Spectral Coarsening Approach to Hypergraph Partitioning
State-of-the-art hypergraph partitioners utilize a multilevel paradigm to construct progressively coarser hypergraphs across multiple layers, guiding cut refinements at each level of the hierarchy. Traditionally, these p…
ClusteringCommunity Detectionhypergraph partitioningVLSI Hypergraph Partitioning with Deep Learning
Partitioning is a known problem in computer science and is critical in chip design workflows, as advancements in this area can significantly influence design quality and efficiency. Deep Learning (DL) techniques, particu…
Deep Learninggraph partitioninghypergraph partitioningTransductive LearningK-SpecPart: Supervised embedding algorithms and cut overlay for improved hypergraph partitioning
State-of-the-art hypergraph partitioners follow the multilevel paradigm that constructs multiple levels of progressively coarser hypergraphs that are used to drive cut refinement on each level of the hierarchy. Multileve…
Dimensionality Reductionhypergraph partitioningSupervised dimensionality reductionScalable Graph Convolutional Network Training on Distributed-Memory Systems
Graph Convolutional Networks (GCNs) are extensively utilized for deep learning on graphs. The large data sizes of graphs and their vertex features make scalable training algorithms and distributed memory systems necessar…
Blockinggraph partitioninghypergraph partitioningHyperEF: Spectral Hypergraph Coarsening by Effective-Resistance Clustering
This paper introduces a scalable algorithmic framework (HyperEF) for spectral coarsening (decomposition) of large-scale hypergraphs by exploiting hyperedge effective resistances. Motivated by the latest theoretical frame…
Clusteringhypergraph partitioningMore Recent Advances in (Hyper)Graph Partitioning
In recent years, significant advances have been made in the design and evaluation of balanced (hyper)graph partitioning algorithms. We survey trends of the last decade in practical algorithms for balanced (hyper)graph pa…
graph partitioninghypergraph partitioningSurveyMultilevel Memetic Hypergraph Partitioning with Greedy Recombination
The Hypergraph Partitioning (HGP) problem is a well-studied problem that finds applications in a variety of domains. The literature on the HGP problem has heavily focused on developing fast heuristic approaches. In sever…
hypergraph partitioningHyperSF: Spectral Hypergraph Coarsening via Flow-based Local Clustering
Hypergraphs allow modeling problems with multi-way high-order relationships. However, the computational cost of most existing hypergraph-based algorithms can be heavily dependent upon the input hypergraph sizes. To addre…
Clusteringhypergraph partitioningBalanced Coarsening for Multilevel Hypergraph Partitioning via Wasserstein Discrepancy
We propose a balanced coarsening scheme for multilevel hypergraph partitioning. In addition, an initial partitioning algorithm is designed to improve the quality of k-way hypergraph partitioning. By assigning vertex weig…
hypergraph partitioningCo-clustering Vertices and Hyperedges via Spectral Hypergraph Partitioning
We propose a novel method to co-cluster the vertices and hyperedges of hypergraphs with edge-dependent vertex weights (EDVWs). In this hypergraph model, the contribution of every vertex to each of its incident hyperedges…
Clusteringhypergraph partitioningHypergraph Partitioning using Tensor Eigenvalue Decomposition
Hypergraphs have gained increasing attention in the machine learning community lately due to their superiority over graphs in capturing super-dyadic interactions among entities. In this work, we propose a novel approach …
graph partitioninghypergraph partitioningMultilevel Acyclic Hypergraph Partitioning
A directed acyclic hypergraph is a generalized concept of a directed acyclic graph, where each hyperedge can contain an arbitrary number of tails and heads. Directed hypergraphs can be used to model data flow and executi…
hypergraph partitioningSchedulingHypergraph Partitioning With Embeddings
Problems in scientific computing, such as distributing large sparse matrix operations, have analogous formulations as hypergraph partitioning problems. A hypergraph is a generalization of a traditional graph wherein "hyp…
hypergraph partitioningQuadratic Decomposable Submodular Function Minimization: Theory and Practice (Computation and Analysis of PageRank over Hypergraphs)
We introduce a new convex optimization problem, termed quadratic decomposable submodular function minimization (QDSFM), which allows to model a number of learning tasks on graphs and hypergraphs. The problem exhibits clo…
hypergraph partitioningBandana: Using Non-volatile Memory for Storing Deep Learning Models
Typical large-scale recommender systems use deep learning models that are stored on a large amount of DRAM. These models often rely on embeddings, which consume most of the required memory. We present Bandana, a storage …
Deep Learninghypergraph partitioningRecommendation SystemsEvolutionary n-level Hypergraph Partitioning with Adaptive Coarsening
Hypergraph partitioning is an NP-hard problem that occurs in many computer science applications where it is necessary to reduce large problems into a number of smaller, computationally tractable sub-problems. Current tec…
Evolutionary Algorithmshypergraph partitioningInhomogeneous Hypergraph Clustering with Applications
Hypergraph partitioning is an important problem in machine learning, computer vision and network analytics. A widely used method for hypergraph partitioning relies on minimizing a normalized sum of the costs of partition…
Clusteringhypergraph partitioningEngineering a direct k-way Hypergraph Partitioning Algorithm
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 …
graph partitioninghypergraph partitioningImproving Coarsening Schemes for Hypergraph Partitioning by Exploiting Community Structure
We present an improved coarsening process for multilevel hypergraph partitioning that incorporates global information about the community structure. Community detection is performed via modularity maximization on a bipar…
Community Detectiongraph partitioninghypergraph partitioningUniform Hypergraph Partitioning: Provable Tensor Methods and Sampling Techniques
In a series of recent works, we have generalised the consistency results in the stochastic block model literature to the case of uniform and non-uniform hypergraphs. The present paper continues the same line of study, wh…
Clusteringhypergraph partitioningStochastic Block Model