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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 1–50 of 208 papers

TitleStatusHype
Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and LocalizationCode2
DistMLIP: A Distributed Inference Platform for Machine Learning Interatomic PotentialsCode2
Accurate and versatile 3D segmentation of plant tissues at cellular resolutionCode1
Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate CommunicationCode1
A Structure-Aware Framework for Learning Device Placements on Computation GraphsCode1
Self-supervised Few-shot Learning for Semantic Segmentation: An Annotation-free ApproachCode1
Unleashing Graph Partitioning for Large-Scale Nearest Neighbor SearchCode1
Learnable Graph Matching: Incorporating Graph Partitioning with Deep Feature Learning for Multiple Object TrackingCode1
Reformulating DOVER-Lap Label Mapping as a Graph Partitioning ProblemCode1
Graph Neural Network Based Coarse-Grained Mapping PredictionCode1
Upsampling DINOv2 features for unsupervised vision tasks and weakly supervised materials segmentationCode1
Fast Multi-view Clustering via Ensembles: Towards Scalability, Superiority, and SimplicityCode1
CuVLER: Enhanced Unsupervised Object Discoveries through Exhaustive Self-Supervised TransformersCode1
Learning to Solve Combinatorial Graph Partitioning Problems via Efficient ExplorationCode1
Generalized Spectral Clustering via Gromov-Wasserstein LearningCode1
Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning and Graph Neural NetworksCode1
RAMA: A Rapid Multicut Algorithm on GPUCode1
Task-specific Scene Structure RepresentationsCode1
A Clustering Method with Graph Maximum Decoding Information—0
A Bayesian Approach To Graph Partitioning—0
DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs—0
A Graph-Partitioning Based Continuous Optimization Approach to Semi-supervised Clustering Problems—0
The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning—0
Correlation clustering with local objectives—0
Deep Learning and Spectral Embedding for Graph Partitioning—0
A Random-Key Optimizer for Combinatorial Optimization—0
Algorithms for metric learning via contrastive embeddings—0
Associating Inter-Image Salient Instances for Weakly Supervised Semantic Segmentation—0
DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks—0
Attribute-Enhanced Similarity Ranking for Sparse Link Prediction—0
DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks—0
A Performance Guarantee for Spectral Clustering—0
Agglomeration of Polygonal Grids using Graph Neural Networks with applications to Multigrid solvers—0
Accelerating Generic Graph Neural Networks via Architecture, Compiler, Partition Method Co-Design—0
A Novel Differentiable Loss Function for Unsupervised Graph Neural Networks in Graph Partitioning—0
Circuit Partitioning for Multi-Core Quantum Architectures with Deep Reinforcement Learning—0
Unsupervised Optimisation of GNNs for Node Clustering—0
GASP, a generalized framework for agglomerative clustering of signed graphs and its application to Instance Segmentation—0
3D Cell Nuclei Segmentation with Balanced Graph Partitioning—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
A Differentiable Approach to Combinatorial Optimization using Dataless Neural Networks—0
COSINE: Compressive Network Embedding on Large-scale Information Networks—0
AGO: Boosting Mobile AI Inference Performance by Removing Constraints on Graph Optimization—0
An Incremental Reseeding Strategy for Clustering—0
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