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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 101–125 of 208 papers

TitleStatusHype
Reinforcement learning for instance segmentation with high-level priors—0
GNNIE: GNN Inference Engine with Load-balancing and Graph-Specific Caching—0
Graph Neural Networks for Inconsistent Cluster Detection in Incremental Entity Resolution—0
DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks—0
Learning Spatial Context with Graph Neural Network for Multi-Person Pose GroupingCode0
Automatic Graph Partitioning for Very Large-scale Deep Learning—0
Endurance-Aware Mapping of Spiking Neural Networks to Neuromorphic Hardware—0
Refining a -nearest neighbor graph for a computationally efficient spectral clusteringCode0
Characterizing and comparing external measures for the assessment of cluster analysis and community detectionCode0
RANK: AI-assisted End-to-End Architecture for Detecting Persistent Attacks in Enterprise Networks—0
Distributed Training of Graph Convolutional Networks using Subgraph Approximation—0
Hypergraph Partitioning using Tensor Eigenvalue Decomposition—0
Optimizing embedding-related quantum annealing parameters for reducing hardware bias—0
Graph-based Topic Extraction from Vector Embeddings of Text Documents: Application to a Corpus of News Articles—0
DistDGL: Distributed Graph Neural Network Training for Billion-Scale GraphsCode0
Accelerating Evolutionary Construction Tree Extraction via Graph Partitioning—0
Joint Semantic Instance Segmentation on Graphs with the Semantic Mutex Watershed—0
A Performance Guarantee for Spectral Clustering—0
Graph Partitioning and Graph Neural Network based Hierarchical Graph Matching for Graph Similarity Computation—0
Deep Affinity Net: Instance Segmentation via Affinity—0
Regular Intersection Emptiness of Graph Problems: Finding a Needle in a Haystack of Graphs with the Help of Automata—0
Proximity Preserving Binary Code using Signed Graph-Cut—0
The Semantic Mutex Watershed for Efficient Bottom-Up Semantic Instance Segmentation—0
Improved Analysis of Spectral Algorithm for Clustering—0
Spectral Modification of Graphs for Improved Spectral ClusteringCode0
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