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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 61–70 of 208 papers

TitleStatusHype
Random projection tree similarity metric for SpectralNetCode0
A parameter-free graph reduction for spectral clustering and SpectralNetCode0
Approximate spectral clustering with eigenvector selection and self-tuned kCode0
Approximate spectral clustering density-based similarity for noisy datasetsCode0
Refining a k-nearest neighbor graph for a computationally efficient spectral clusteringCode0
Graph Construction using Principal Axis Trees for Simple Graph ConvolutionCode0
Random Projection Forest Initialization for Graph Convolutional NetworksCode0
Fair and skill-diverse student group formation via constrained k-way graph partitioning—0
Task-specific Scene Structure RepresentationsCode1
Scalable Graph Convolutional Network Training on Distributed-Memory Systems—0
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