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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 101–150 of 208 papers

TitleStatusHype
Stochastic Blockmodeling for Online Advertising—0
Sub-Graph Learning for Spatiotemporal Forecasting via Knowledge Distillation—0
SUGAR: Efficient Subgraph-level Training via Resource-aware Graph Partitioning—0
Supporting Very Large Models using Automatic Dataflow Graph Partitioning—0
The Mutex Watershed: Efficient, Parameter-Free Image Partitioning—0
The Semantic Mutex Watershed for Efficient Bottom-Up Semantic Instance Segmentation—0
Towards Efficient Large-Scale Graph Neural Network Computing—0
Trading Quality for Efficiency of Graph Partitioning: An Inductive Method across Graphs—0
Uplifting the Expressive Power of Graph Neural Networks through Graph Partitioning—0
VLSI Hypergraph Partitioning with Deep Learning—0
WaveGAS: Waveform Relaxation for Scaling Graph Neural Networks—0
WawPart: Workload-Aware Partitioning of Knowledge Graphs—0
Weighted Laplacian and Its Theoretical Applications—0
The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning—0
Distributed Power-law Graph Computing: Theoretical and Empirical Analysis—0
Distributed Training of Graph Convolutional Networks using Subgraph Approximation—0
Distributed Training of Large Graph Neural Networks with Variable Communication Rates—0
Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning—0
Dual-Bounded Nonlinear Optimal Transport for Size Constrained Min Cut Clustering—0
Edge-set reduction to efficiently solve the graph partitioning problem with the genetic algorithm—0
Initialization for Network Embedding: A Graph Partition Approach—0
Efficient Partitioning Method of Large-Scale Public Safety Spatio-Temporal Data based on Information Loss Constraints—0
Efficient Video Segmentation Using Parametric Graph Partitioning—0
_2-norm Flow Diffusion in Near-Linear Time—0
Endurance-Aware Mapping of Spiking Neural Networks to Neuromorphic Hardware—0
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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