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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 21–30 of 208 papers

TitleStatusHype
BatchGNN: Efficient CPU-Based Distributed GNN Training on Very Large Graphs—0
Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning—0
AGO: Boosting Mobile AI Inference Performance by Removing Constraints on Graph Optimization—0
The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning—0
A Graph-Partitioning Based Continuous Optimization Approach to Semi-supervised Clustering Problems—0
A Random-Key Optimizer for Combinatorial Optimization—0
Algorithms for metric learning via contrastive embeddings—0
Associating Inter-Image Salient Instances for Weakly Supervised Semantic Segmentation—0
Automatic Graph Partitioning for Very Large-scale Deep Learning—0
Biologically-Constrained Graphs for Global Connectomics Reconstruction—0
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