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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 91–100 of 208 papers

TitleStatusHype
A Design Flow for Mapping Spiking Neural Networks to Many-Core Neuromorphic Hardware—0
Stateless actor-critic for instance segmentation with high-level priors—0
xER: An Explainable Model for Entity Resolution using an Efficient Solution for the Clique Partitioning Problem—0
_2-norm Flow Diffusion in Near-Linear Time—0
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
Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning and Graph Neural NetworksCode1
Learning Spatial Context with Graph Neural Network for Multi-Person Pose GroupingCode0
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