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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 91–100 of 208 papers

TitleStatusHype
From Text to Topics in Healthcare Records: An Unsupervised Graph Partitioning Methodology—0
Consistency of Spectral Partitioning of Uniform Hypergraphs under Planted Partition Model—0
Content-driven, unsupervised clustering of news articles through multiscale graph partitioning—0
Generalized Spectral Clustering for Directed and Undirected Graphs—0
BatchGNN: Efficient CPU-Based Distributed GNN Training on Very Large Graphs—0
A Design Flow for Mapping Spiking Neural Networks to Many-Core Neuromorphic Hardware—0
GNNIE: GNN Inference Engine with Load-balancing and Graph-Specific Caching—0
Graph-based Topic Extraction from Vector Embeddings of Text Documents: Application to a Corpus of News Articles—0
AGO: Boosting Mobile AI Inference Performance by Removing Constraints on Graph Optimization—0
Distributed Training of Graph Convolutional Networks using Subgraph Approximation—0
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