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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 181–190 of 208 papers

TitleStatusHype
Deep Affinity Net: Instance Segmentation via Affinity—0
Deep Learning and Spectral Embedding for Graph Partitioning—0
DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks—0
DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs—0
DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks—0
Distributed Compressed Sparse Row Format for Spiking Neural Network Simulation, Serialization, and Interoperability—0
Distributed Power-law Graph Computing: Theoretical and Empirical Analysis—0
Distributed Training of Graph Convolutional Networks using Subgraph Approximation—0
Distributed Training of Large Graph Neural Networks with Variable Communication Rates—0
Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning—0
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