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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 131–140 of 208 papers

TitleStatusHype
The Mutex Watershed: Efficient, Parameter-Free Image Partitioning—0
The Semantic Mutex Watershed for Efficient Bottom-Up Semantic Instance Segmentation—0
Towards Efficient Large-Scale Graph Neural Network Computing—0
Trading Quality for Efficiency of Graph Partitioning: An Inductive Method across Graphs—0
Uplifting the Expressive Power of Graph Neural Networks through Graph Partitioning—0
VLSI Hypergraph Partitioning with Deep Learning—0
WaveGAS: Waveform Relaxation for Scaling Graph Neural Networks—0
WawPart: Workload-Aware Partitioning of Knowledge Graphs—0
Weighted Laplacian and Its Theoretical Applications—0
The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning—0
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