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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
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
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