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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 31–40 of 208 papers

TitleStatusHype
Correlation clustering with local objectives—0
A Performance Guarantee for Spectral Clustering—0
Agglomeration of Polygonal Grids using Graph Neural Networks with applications to Multigrid solvers—0
Accelerating Generic Graph Neural Networks via Architecture, Compiler, Partition Method Co-Design—0
A Novel Differentiable Loss Function for Unsupervised Graph Neural Networks in Graph Partitioning—0
Circuit Partitioning for Multi-Core Quantum Architectures with Deep Reinforcement Learning—0
Unsupervised Optimisation of GNNs for Node Clustering—0
GASP, a generalized framework for agglomerative clustering of signed graphs and its application to Instance Segmentation—0
3D Cell Nuclei Segmentation with Balanced Graph Partitioning—0
A Differentiable Approach to Combinatorial Optimization using Dataless Neural Networks—0
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