hypergraph partitioning
6 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in hypergraph partitioning
Latest papers with no code
K-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.
Scalable Graph Convolutional Network Training on Distributed-Memory Systems
The large data sizes of graphs and their vertex features make scalable training algorithms and distributed memory systems necessary.
More 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.
Multilevel Memetic Hypergraph Partitioning with Greedy Recombination
The performance of our algorithm is compared with the state-of-the-art HGP algorithms on $150$ real-life instances taken from the benchmark datasets used in the literature.
Balanced Coarsening for Multilevel Hypergraph Partitioning via Wasserstein Discrepancy
We propose a balanced coarsening scheme for multilevel hypergraph partitioning.
Streaming Hypergraph Partitioning Algorithms on Limited Memory Environments
For many of today's data-centric applications, this data is streaming; new items arrive continuously, and the data grows with time.
Hypergraph Partitioning using Tensor Eigenvalue Decomposition
We also show improvement for the min-cut solution on 2-uniform hypergraphs (graphs) over the standard spectral partitioning algorithm.
Multilevel Acyclic Hypergraph Partitioning
The acyclic hypergraph partitioning problem is to partition the hypernodes of a directed acyclic hypergraph into a given number of blocks of roughly equal size such that the corresponding quotient graph is acyclic while minimizing an objective function on the partition.
Hypergraph Partitioning With Embeddings
As a result, hypergraph partitioning is an NP-Hard problem to both solve or approximate.
Quadratic 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.
