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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 51–75 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
Spectral Normalized-Cut Graph Partitioning with Fairness ConstraintsCode0
Edge-set reduction to efficiently solve the graph partitioning problem with the genetic algorithm—0
PolicyClusterGCN: Identifying Efficient Clusters for Training Graph Convolutional Networks—0
BatchGNN: Efficient CPU-Based Distributed GNN Training on Very Large Graphs—0
Efficient Partitioning Method of Large-Scale Public Safety Spatio-Temporal Data based on Information Loss Constraints—0
Creating Multi-Level Skill Hierarchies in Reinforcement LearningCode0
Fast Algorithms for Directed Graph Partitioning Using Flows and Reweighted Eigenvalues—0
One-step Bipartite Graph Cut: A Normalized Formulation and Its Application to Scalable Subspace Clustering—0
Distributed Compressed Sparse Row Format for Spiking Neural Network Simulation, Serialization, and Interoperability—0
Inductive Graph UnlearningCode0
Distributed Graph Embedding with Information-Oriented Random WalksCode0
A parameter-free graph reduction for spectral clustering and SpectralNetCode0
Random projection tree similarity metric for SpectralNetCode0
Random Projection Forest Initialization for Graph Convolutional NetworksCode0
Approximate spectral clustering with eigenvector selection and self-tuned kCode0
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