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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 51–60 of 208 papers

TitleStatusHype
Large Scale Training of Graph Neural Networks for Optimal Markov-Chain Partitioning Using the Kemeny Constant—0
Uplifting the Expressive Power of Graph Neural Networks through Graph Partitioning—0
A Novel Differentiable Loss Function for Unsupervised Graph Neural Networks in Graph Partitioning—0
BClean: A Bayesian Data Cleaning SystemCode0
NeuroCUT: A Neural Approach for Robust Graph PartitioningCode0
Mitigating Pilot Contamination and Enabling IoT Scalability in Massive MIMO Systems—0
DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks—0
An Experimental Comparison of Partitioning Strategies for Distributed Graph Neural Network Training—0
Accelerating Generic Graph Neural Networks via Architecture, Compiler, Partition Method Co-Design—0
Federated Classification in Hyperbolic Spaces via Secure Aggregation of Convex HullsCode0
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