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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 126–150 of 208 papers

TitleStatusHype
Correlation clustering with local objectives—0
Weighted Laplacian and Its Theoretical Applications—0
Generalizable Resource Allocation in Stream Processing via Deep Reinforcement LearningCode0
Graph-Partitioning-Based Diffusion Convolutional Recurrent Neural Network for Large-Scale Traffic ForecastingCode0
Initialization for Network Embedding: A Graph Partition Approach—0
A 3D Convolutional Approach to Spectral Object Segmentation in Space and TimeCode0
Graph Embeddings at Scale—0
GASP, a generalized framework for agglomerative clustering of signed graphs and its application to Instance Segmentation—0
Biologically-Constrained Graphs for Global Connectomics Reconstruction—0
Leveraging Domain Knowledge to Improve Microscopy Image Segmentation with Lifted MulticutsCode0
Scalable Gromov-Wasserstein Learning for Graph Partitioning and MatchingCode0
The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning—0
PyTorch-BigGraph: A Large-scale Graph Embedding SystemCode0
GAP: Generalizable Approximate Graph Partitioning FrameworkCode0
Massively Parallel Benders Decomposition for Correlation Clustering—0
Learning Space Partitions for Nearest Neighbor SearchCode0
COSINE: Compressive Network Embedding on Large-scale Information Networks—0
From Free Text to Clusters of Content in Health Records: An Unsupervised Graph Partitioning Approach—0
Mean-field theory of graph neural networks in graph partitioning—0
Towards Efficient Large-Scale Graph Neural Network Computing—0
The Mutex Watershed: Efficient, Parameter-Free Image Partitioning—0
Associating Inter-Image Salient Instances for Weakly Supervised Semantic Segmentation—0
Content-driven, unsupervised clustering of news articles through multiscale graph partitioning—0
Supporting Very Large Models using Automatic Dataflow Graph Partitioning—0
Algorithms for metric learning via contrastive embeddings—0
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