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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 151–200 of 208 papers

TitleStatusHype
A Graph-Partitioning Based Continuous Optimization Approach to Semi-supervised Clustering Problems—0
Algorithms for metric learning via contrastive embeddings—0
An Effective Branch-and-Bound Algorithm with New Bounding Methods for the Maximum s-Bundle Problem—0
A new cut-based genetic algorithm for graph partitioning applied to cell formation—0
An Experimental Comparison of Partitioning Strategies for Distributed Graph Neural Network Training—0
An Incremental Reseeding Strategy for Clustering—0
Unsupervised Optimisation of GNNs for Node Clustering—0
A Novel Differentiable Loss Function for Unsupervised Graph Neural Networks in Graph Partitioning—0
A Performance Guarantee for Spectral Clustering—0
A Random-Key Optimizer for Combinatorial Optimization—0
Associating Inter-Image Salient Instances for Weakly Supervised Semantic Segmentation—0
Attribute-Enhanced Similarity Ranking for Sparse Link Prediction—0
Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning—0
Automatic Graph Partitioning for Very Large-scale Deep Learning—0
BatchGNN: Efficient CPU-Based Distributed GNN Training on Very Large Graphs—0
Biologically-Constrained Graphs for Global Connectomics Reconstruction—0
CATGNN: Cost-Efficient and Scalable Distributed Training for Graph Neural Networks—0
Circuit Partitioning for Multi-Core Quantum Architectures with Deep Reinforcement Learning—0
Classifier Based Graph Construction for Video Segmentation—0
Clustering and Community Detection with Imbalanced Clusters—0
Combining Multiple Clusterings via Crowd Agreement Estimation and Multi-Granularity Link Analysis—0
Compassionately Conservative Balanced Cuts for Image Segmentation—0
Consistency of Spectral Hypergraph Partitioning under Planted Partition Model—0
Consistency of Spectral Partitioning of Uniform Hypergraphs under Planted Partition Model—0
Content-driven, unsupervised clustering of news articles through multiscale graph partitioning—0
Correlation clustering with local objectives—0
COSINE: Compressive Network Embedding on Large-scale Information Networks—0
Cross-Document Co-Reference Resolution using Sample-Based Clustering with Knowledge Enrichment—0
Data Clustering and Graph Partitioning via Simulated Mixing—0
Deep Affinity Net: Instance Segmentation via Affinity—0
Deep Learning and Spectral Embedding for Graph Partitioning—0
DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks—0
DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks—0
Distributed Compressed Sparse Row Format for Spiking Neural Network Simulation, Serialization, and Interoperability—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
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