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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
Stochastic Blockmodeling for Online Advertising—0
Sub-Graph Learning for Spatiotemporal Forecasting via Knowledge Distillation—0
SUGAR: Efficient Subgraph-level Training via Resource-aware Graph Partitioning—0
Supporting Very Large Models using Automatic Dataflow Graph Partitioning—0
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
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