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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 176–200 of 208 papers

TitleStatusHype
Efficient Video Segmentation Using Parametric Graph Partitioning—0
Learning Communities in the Presence of Errors—0
Classifier Based Graph Construction for Video Segmentation—0
Graph Partitioning via Parallel Submodular Approximation to Accelerate Distributed Machine Learning—0
Relations Between Adjacency and Modularity Graph Partitioning—0
Consistency of Spectral Hypergraph Partitioning under Planted Partition Model—0
Incorporating Road Networks into Territory Design—0
Graph Partitioning for Independent Sets—0
Integrating Graph Partitioning and Matching for Trajectory Analysis in Video Surveillance—0
Cross-Document Co-Reference Resolution using Sample-Based Clustering with Knowledge Enrichment—0
Distributed Power-law Graph Computing: Theoretical and Empirical Analysis—0
Consistency of Spectral Partitioning of Uniform Hypergraphs under Planted Partition Model—0
FGPGA: An Efficient Genetic Approach for Producing Feasible Graph Partitions—0
Stochastic Blockmodeling for Online Advertising—0
Similarity-Driven Semantic Role Induction via Graph Partitioning—0
Resolution-limit-free and local Non-negative Matrix Factorization quality functions for graph clustering—0
An Incremental Reseeding Strategy for Clustering—0
Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning—0
Orientation Robust Text Line Detection in Natural Images—0
Combining Multiple Clusterings via Crowd Agreement Estimation and Multi-Granularity Link Analysis—0
Parallel Graph Partitioning for Complex NetworksCode0
Multiscale Dictionary Learning for Estimating Conditional Distributions—0
Spectral Clustering with Imbalanced Data—0
Minimal Dirichlet energy partitions for graphs—0
Flow-Based Algorithms for Local Graph Clustering—0
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