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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 76–100 of 208 papers

TitleStatusHype
_2-norm Flow Diffusion in Near-Linear Time—0
Endurance-Aware Mapping of Spiking Neural Networks to Neuromorphic Hardware—0
Engineering a direct k-way Hypergraph Partitioning Algorithm—0
Evolutionary Acyclic Graph Partitioning—0
Circuit Partitioning for Multi-Core Quantum Architectures with Deep Reinforcement Learning—0
Fair and skill-diverse student group formation via constrained k-way graph partitioning—0
Classifier Based Graph Construction for Video Segmentation—0
Fast Algorithms for Directed Graph Partitioning Using Flows and Reweighted Eigenvalues—0
FastGAS: Fast Graph-based Annotation Selection for In-Context Learning—0
BatchGNN: Efficient CPU-Based Distributed GNN Training on Very Large Graphs—0
Combining Multiple Clusterings via Crowd Agreement Estimation and Multi-Granularity Link Analysis—0
FGPGA: An Efficient Genetic Approach for Producing Feasible Graph Partitions—0
Fine-Grained Bipartite Concept Factorization for Clustering—0
Flow-Based Algorithms for Local Graph Clustering—0
From Free Text to Clusters of Content in Health Records: An Unsupervised Graph Partitioning Approach—0
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
A Design Flow for Mapping Spiking Neural Networks to Many-Core Neuromorphic Hardware—0
Distributed Training of Graph Convolutional Networks using Subgraph Approximation—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 Power-law Graph Computing: Theoretical and Empirical Analysis—0
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