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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 101–150 of 208 papers

TitleStatusHype
Nimble GNN Embedding with Tensor-Train Decomposition—0
Node-level Contrastive Unlearning on Graph Neural Networks—0
One-step Bipartite Graph Cut: A Normalized Formulation and Its Application to Scalable Subspace Clustering—0
On Hash-Based Work Distribution Methods for Parallel Best-First Search—0
On the definition of Shape Parts: a Dominant Sets Approach—0
Optimizing embedding-related quantum annealing parameters for reducing hardware bias—0
Orientation Robust Text Line Detection in Natural Images—0
PolicyClusterGCN: Identifying Efficient Clusters for Training Graph Convolutional Networks—0
Preconditioned Spectral Clustering for Stochastic Block Partition Streaming Graph Challenge—0
Proximity Preserving Binary Code using Signed Graph-Cut—0
Quantum Annealing based Power Grid Partitioning for Parallel Simulation—0
RANK: AI-assisted End-to-End Architecture for Detecting Persistent Attacks in Enterprise Networks—0
Recent Progress on Graph Partitioning Problems Using Evolutionary Computation—0
Recursive Decomposition for Nonconvex Optimization—0
Regular Intersection Emptiness of Graph Problems: Finding a Needle in a Haystack of Graphs with the Help of Automata—0
Regularized Co-Clustering with Dual Supervision—0
Reinforcement learning for instance segmentation with high-level priors—0
Relations Between Adjacency and Modularity Graph Partitioning—0
Replica Symmetry Breaking in Bipartite Spin Glasses and Neural Networks—0
Resolution-limit-free and local Non-negative Matrix Factorization quality functions for graph clustering—0
Revisiting Graph Construction for Fast Image Segmentation—0
Scalable Graph Convolutional Network Training on Distributed-Memory Systems—0
Similarity-Driven Semantic Role Induction via Graph Partitioning—0
Spectral Clustering with Imbalanced Data—0
Spectral Hashing—0
Stateless actor-critic for instance segmentation with high-level priors—0
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
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