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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
Stochastic Blockmodeling for Online Advertising—0
Sub-Graph Learning for Spatiotemporal Forecasting via Knowledge Distillation—0
SUGAR: Efficient Subgraph-level Training via Resource-aware Graph Partitioning—0
Supporting Very Large Models using Automatic Dataflow Graph Partitioning—0
The Mutex Watershed: Efficient, Parameter-Free Image Partitioning—0
The Semantic Mutex Watershed for Efficient Bottom-Up Semantic Instance Segmentation—0
Towards Efficient Large-Scale Graph Neural Network Computing—0
Trading Quality for Efficiency of Graph Partitioning: An Inductive Method across Graphs—0
Uplifting the Expressive Power of Graph Neural Networks through Graph Partitioning—0
VLSI Hypergraph Partitioning with Deep Learning—0
WaveGAS: Waveform Relaxation for Scaling Graph Neural Networks—0
WawPart: Workload-Aware Partitioning of Knowledge Graphs—0
Weighted Laplacian and Its Theoretical Applications—0
The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning—0
xER: An Explainable Model for Entity Resolution using an Efficient Solution for the Clique Partitioning Problem—0
3D Cell Nuclei Segmentation with Balanced Graph Partitioning—0
A Bayesian Approach To Graph Partitioning—0
Accelerating Evolutionary Construction Tree Extraction via Graph Partitioning—0
Accelerating Generic Graph Neural Networks via Architecture, Compiler, Partition Method Co-Design—0
A Clustering Method with Graph Maximum Decoding Information—0
A Design Flow for Mapping Spiking Neural Networks to Many-Core Neuromorphic Hardware—0
A Differentiable Approach to Combinatorial Optimization using Dataless Neural Networks—0
GASP, a generalized framework for agglomerative clustering of signed graphs and its application to Instance Segmentation—0
Agglomeration of Polygonal Grids using Graph Neural Networks with applications to Multigrid solvers—0
AGO: Boosting Mobile AI Inference Performance by Removing Constraints on Graph Optimization—0
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