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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 11–20 of 208 papers

TitleStatusHype
Accurate and versatile 3D segmentation of plant tissues at cellular resolutionCode1
Learnable Graph Matching: Incorporating Graph Partitioning with Deep Feature Learning for Multiple Object TrackingCode1
Self-supervised Few-shot Learning for Semantic Segmentation: An Annotation-free ApproachCode1
Unleashing Graph Partitioning for Large-Scale Nearest Neighbor SearchCode1
Fast Multi-view Clustering via Ensembles: Towards Scalability, Superiority, and SimplicityCode1
Generalized Spectral Clustering via Gromov-Wasserstein LearningCode1
A Structure-Aware Framework for Learning Device Placements on Computation GraphsCode1
Graph Neural Network Based Coarse-Grained Mapping PredictionCode1
A Min-max Cult Algorithm for Graph Partitioning and Data ClusteringCode0
DistDGL: Distributed Graph Neural Network Training for Billion-Scale GraphsCode0
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