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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
CATGNN: Cost-Efficient and Scalable Distributed Training for Graph Neural Networks—0
Distributed Compressed Sparse Row Format for Spiking Neural Network Simulation, Serialization, and Interoperability—0
Biologically-Constrained Graphs for Global Connectomics Reconstruction—0
An Experimental Comparison of Partitioning Strategies for Distributed Graph Neural Network Training—0
Efficient Video Segmentation Using Parametric Graph Partitioning—0
Dual-Bounded Nonlinear Optimal Transport for Size Constrained Min Cut Clustering—0
A new cut-based genetic algorithm for graph partitioning applied to cell formation—0
Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning—0
Distributed Training of Large Graph Neural Networks with Variable Communication Rates—0
BatchGNN: Efficient CPU-Based Distributed GNN Training on Very Large Graphs—0
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