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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 101–110 of 208 papers

TitleStatusHype
Reinforcement learning for instance segmentation with high-level priors—0
GNNIE: GNN Inference Engine with Load-balancing and Graph-Specific Caching—0
Graph Neural Networks for Inconsistent Cluster Detection in Incremental Entity Resolution—0
DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks—0
Learning Spatial Context with Graph Neural Network for Multi-Person Pose GroupingCode0
Automatic Graph Partitioning for Very Large-scale Deep Learning—0
Endurance-Aware Mapping of Spiking Neural Networks to Neuromorphic Hardware—0
Refining a -nearest neighbor graph for a computationally efficient spectral clusteringCode0
Characterizing and comparing external measures for the assessment of cluster analysis and community detectionCode0
RANK: AI-assisted End-to-End Architecture for Detecting Persistent Attacks in Enterprise Networks—0
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