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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
PGT-I: Scaling Spatiotemporal GNNs with Memory-Efficient Distributed TrainingCode0
Demystifying Distributed Training of Graph Neural Networks for Link PredictionCode0
DistMLIP: A Distributed Inference Platform for Machine Learning Interatomic PotentialsCode2
Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning—0
A Graph-Partitioning Based Continuous Optimization Approach to Semi-supervised Clustering Problems—0
Node-level Contrastive Unlearning on Graph Neural Networks—0
WaveGAS: Waveform Relaxation for Scaling Graph Neural Networks—0
Dual-Bounded Nonlinear Optimal Transport for Size Constrained Min Cut Clustering—0
Hierarchical Superpixel Segmentation via Structural Information TheoryCode0
FairGP: A Scalable and Fair Graph Transformer Using Graph PartitioningCode0
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