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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 26–50 of 208 papers

TitleStatusHype
A Random-Key Optimizer for Combinatorial Optimization—0
Algorithms for metric learning via contrastive embeddings—0
Associating Inter-Image Salient Instances for Weakly Supervised Semantic Segmentation—0
Deep Learning and Spectral Embedding for Graph Partitioning—0
Attribute-Enhanced Similarity Ranking for Sparse Link Prediction—0
DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks—0
A Performance Guarantee for Spectral Clustering—0
Agglomeration of Polygonal Grids using Graph Neural Networks with applications to Multigrid solvers—0
Accelerating Generic Graph Neural Networks via Architecture, Compiler, Partition Method Co-Design—0
A Novel Differentiable Loss Function for Unsupervised Graph Neural Networks in Graph Partitioning—0
Circuit Partitioning for Multi-Core Quantum Architectures with Deep Reinforcement Learning—0
Unsupervised Optimisation of GNNs for Node Clustering—0
GASP, a generalized framework for agglomerative clustering of signed graphs and its application to Instance Segmentation—0
3D Cell Nuclei Segmentation with Balanced Graph Partitioning—0
Classifier Based Graph Construction for Video Segmentation—0
Clustering and Community Detection with Imbalanced Clusters—0
Combining Multiple Clusterings via Crowd Agreement Estimation and Multi-Granularity Link Analysis—0
Compassionately Conservative Balanced Cuts for Image Segmentation—0
Consistency of Spectral Hypergraph Partitioning under Planted Partition Model—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
A Differentiable Approach to Combinatorial Optimization using Dataless Neural Networks—0
An Incremental Reseeding Strategy for Clustering—0
AGO: Boosting Mobile AI Inference Performance by Removing Constraints on Graph Optimization—0
Cross-Document Co-Reference Resolution using Sample-Based Clustering with Knowledge Enrichment—0
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