Pinned Repositories
kahypar
KaHyPar (Karlsruhe Hypergraph Partitioning) is a multilevel hypergraph partitioning framework providing direct k-way and recursive bisection based partitioning algorithms that compute solutions of very high quality.
mt-kahypar
Mt-KaHyPar (Multi-Threaded Karlsruhe Hypergraph Partitioner) is a shared-memory multilevel graph and hypergraph partitioner equipped with parallel implementations of techniques used in the best sequential partitioning algorithms. Mt-KaHyPar can partition extremely large hypergraphs very fast and with high quality.
BLISS
Large Scale Search Index
ClusterScripts
A script to run a bunch of tasks on BWUniCluster
gp-ann
Experimental Code for "Unleashing Graph Partitioning for Large-Scale Nearest Neighbor Search"
HGPEvalScripts
A collection of scripts to generate plots for evaluating HGP tools
learn-to-hash
Neural LSH [ICLR 2020] - Using supervised learning to produce better space partitions for fast nearest neighbor search.
networkit
NetworKit is a growing open-source toolkit for large-scale network analysis.
practical-expander-decomposition
WHFC
Weighted HyperFlowCutter
larsgottesbueren's Repositories
larsgottesbueren/gp-ann
Experimental Code for "Unleashing Graph Partitioning for Large-Scale Nearest Neighbor Search"
larsgottesbueren/WHFC
Weighted HyperFlowCutter
larsgottesbueren/practical-expander-decomposition
larsgottesbueren/BLISS
Large Scale Search Index
larsgottesbueren/ClusterScripts
A script to run a bunch of tasks on BWUniCluster
larsgottesbueren/HGPEvalScripts
A collection of scripts to generate plots for evaluating HGP tools
larsgottesbueren/learn-to-hash
Neural LSH [ICLR 2020] - Using supervised learning to produce better space partitions for fast nearest neighbor search.
larsgottesbueren/networkit
NetworKit is a growing open-source toolkit for large-scale network analysis.
larsgottesbueren/Neural-Partitioner
Unsupervised Space Partitioning for Nearest Neighbor Search: Using a custom loss function to train ML models to learn partitions of high-dimensional and large datasets.