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graph partitioning

Graph Partitioning is generally the first step of distributed graph computing tasks. The targets are load-balance and minimizing the communication volume.

Papers

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Showing 126–150 of 208 papers

TitleStatusHype
Engineering a direct k-way Hypergraph Partitioning Algorithm—0
Evolutionary Acyclic Graph Partitioning—0
Fair and skill-diverse student group formation via constrained k-way graph partitioning—0
Fast Algorithms for Directed Graph Partitioning Using Flows and Reweighted Eigenvalues—0
FastGAS: Fast Graph-based Annotation Selection for In-Context Learning—0
FGPGA: An Efficient Genetic Approach for Producing Feasible Graph Partitions—0
Fine-Grained Bipartite Concept Factorization for Clustering—0
Flow-Based Algorithms for Local Graph Clustering—0
From Free Text to Clusters of Content in Health Records: An Unsupervised Graph Partitioning Approach—0
From Text to Topics in Healthcare Records: An Unsupervised Graph Partitioning Methodology—0
Generalized Spectral Clustering for Directed and Undirected Graphs—0
GLISP: A Scalable GNN Learning System by Exploiting Inherent Structural Properties of Graphs—0
GNNIE: GNN Inference Engine with Load-balancing and Graph-Specific Caching—0
Graph-based Topic Extraction from Vector Embeddings of Text Documents: Application to a Corpus of News Articles—0
Graph Embeddings at Scale—0
Graph Neural Networks for Inconsistent Cluster Detection in Incremental Entity Resolution—0
Graph Neural Networks on Graph Databases—0
Graph Partitioning and Graph Neural Network based Hierarchical Graph Matching for Graph Similarity Computation—0
Graph Partitioning for Independent Sets—0
Graph Partitioning via Parallel Submodular Approximation to Accelerate Distributed Machine Learning—0
Graph Partitioning with Acyclicity Constraints—0
Hierarchical Clustering via Spreading Metrics—0
Higher-Order Correlation Clustering for Image Segmentation—0
Hypergraph Partitioning using Tensor Eigenvalue Decomposition—0
Improved Analysis of Spectral Algorithm for Clustering—0
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