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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 111–120 of 208 papers

TitleStatusHype
Distributed Training of Graph Convolutional Networks using Subgraph Approximation—0
Hypergraph Partitioning using Tensor Eigenvalue Decomposition—0
Optimizing embedding-related quantum annealing parameters for reducing hardware bias—0
Graph-based Topic Extraction from Vector Embeddings of Text Documents: Application to a Corpus of News Articles—0
DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs—0
Accelerating Evolutionary Construction Tree Extraction via Graph Partitioning—0
Joint Semantic Instance Segmentation on Graphs with the Semantic Mutex Watershed—0
A Performance Guarantee for Spectral Clustering—0
Graph Partitioning and Graph Neural Network based Hierarchical Graph Matching for Graph Similarity Computation—0
Deep Affinity Net: Instance Segmentation via Affinity—0
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