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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 161–170 of 208 papers

TitleStatusHype
A Random-Key Optimizer for Combinatorial Optimization—0
Associating Inter-Image Salient Instances for Weakly Supervised Semantic Segmentation—0
Attribute-Enhanced Similarity Ranking for Sparse Link Prediction—0
Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning—0
Automatic Graph Partitioning for Very Large-scale Deep Learning—0
BatchGNN: Efficient CPU-Based Distributed GNN Training on Very Large Graphs—0
Biologically-Constrained Graphs for Global Connectomics Reconstruction—0
CATGNN: Cost-Efficient and Scalable Distributed Training for Graph Neural Networks—0
Circuit Partitioning for Multi-Core Quantum Architectures with Deep Reinforcement Learning—0
Classifier Based Graph Construction for Video Segmentation—0
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