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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 1–10 of 208 papers

TitleStatusHype
DistMLIP: A Distributed Inference Platform for Machine Learning Interatomic PotentialsCode2
Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and LocalizationCode2
Graph Neural Network Based Coarse-Grained Mapping PredictionCode1
Generalized Spectral Clustering via Gromov-Wasserstein LearningCode1
Accurate and versatile 3D segmentation of plant tissues at cellular resolutionCode1
A Structure-Aware Framework for Learning Device Placements on Computation GraphsCode1
CuVLER: Enhanced Unsupervised Object Discoveries through Exhaustive Self-Supervised TransformersCode1
Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate CommunicationCode1
Fast Multi-view Clustering via Ensembles: Towards Scalability, Superiority, and SimplicityCode1
Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning and Graph Neural NetworksCode1
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