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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 71–80 of 208 papers

TitleStatusHype
DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks—0
Attribute-Enhanced Similarity Ranking for Sparse Link Prediction—0
DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks—0
Associating Inter-Image Salient Instances for Weakly Supervised Semantic Segmentation—0
Algorithms for metric learning via contrastive embeddings—0
A Clustering Method with Graph Maximum Decoding Information—0
Deep Learning and Spectral Embedding for Graph Partitioning—0
Deep Affinity Net: Instance Segmentation via Affinity—0
A Random-Key Optimizer for Combinatorial Optimization—0
Data Clustering and Graph Partitioning via Simulated Mixing—0
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