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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 1–25 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
Self-supervised Few-shot Learning for Semantic Segmentation: An Annotation-free ApproachCode1
Upsampling DINOv2 features for unsupervised vision tasks and weakly supervised materials segmentationCode1
RAMA: A Rapid Multicut Algorithm on GPUCode1
Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning and Graph Neural NetworksCode1
Generalized Spectral Clustering via Gromov-Wasserstein LearningCode1
CuVLER: Enhanced Unsupervised Object Discoveries through Exhaustive Self-Supervised TransformersCode1
Reformulating DOVER-Lap Label Mapping as a Graph Partitioning ProblemCode1
Learnable Graph Matching: Incorporating Graph Partitioning with Deep Feature Learning for Multiple Object TrackingCode1
Unleashing Graph Partitioning for Large-Scale Nearest Neighbor SearchCode1
Task-specific Scene Structure RepresentationsCode1
Learning to Solve Combinatorial Graph Partitioning Problems via Efficient ExplorationCode1
A Structure-Aware Framework for Learning Device Placements on Computation GraphsCode1
Fast Multi-view Clustering via Ensembles: Towards Scalability, Superiority, and SimplicityCode1
Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate CommunicationCode1
Graph Neural Network Based Coarse-Grained Mapping PredictionCode1
A Min-max Cult Algorithm for Graph Partitioning and Data ClusteringCode0
Ego-splitting Framework: from Non-Overlapping to Overlapping ClustersCode0
Exploring Key Point Analysis with Pairwise Generation and Graph PartitioningCode0
Distributed Graph Embedding with Information-Oriented Random WalksCode0
Approximate spectral clustering with eigenvector selection and self-tuned kCode0
Approximate spectral clustering density-based similarity for noisy datasetsCode0
Fine-tuning Partition-aware Item Similarities for Efficient and Scalable RecommendationCode0
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