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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 51–100 of 208 papers

TitleStatusHype
PolicyClusterGCN: Identifying Efficient Clusters for Training Graph Convolutional Networks—0
BatchGNN: Efficient CPU-Based Distributed GNN Training on Very Large Graphs—0
Efficient Partitioning Method of Large-Scale Public Safety Spatio-Temporal Data based on Information Loss Constraints—0
Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate CommunicationCode1
Creating Multi-Level Skill Hierarchies in Reinforcement LearningCode0
Fast Algorithms for Directed Graph Partitioning Using Flows and Reweighted Eigenvalues—0
One-step Bipartite Graph Cut: A Normalized Formulation and Its Application to Scalable Subspace Clustering—0
Distributed Compressed Sparse Row Format for Spiking Neural Network Simulation, Serialization, and Interoperability—0
Inductive Graph UnlearningCode0
Distributed Graph Embedding with Information-Oriented Random WalksCode0
A parameter-free graph reduction for spectral clustering and SpectralNetCode0
Random projection tree similarity metric for SpectralNetCode0
Refining a k-nearest neighbor graph for a computationally efficient spectral clusteringCode0
Approximate spectral clustering with eigenvector selection and self-tuned kCode0
Graph Construction using Principal Axis Trees for Simple Graph ConvolutionCode0
Approximate spectral clustering density-based similarity for noisy datasetsCode0
Random Projection Forest Initialization for Graph Convolutional NetworksCode0
Fair and skill-diverse student group formation via constrained k-way graph partitioning—0
Task-specific Scene Structure RepresentationsCode1
Scalable Graph Convolutional Network Training on Distributed-Memory Systems—0
AGO: Boosting Mobile AI Inference Performance by Removing Constraints on Graph Optimization—0
Sub-Graph Learning for Spatiotemporal Forecasting via Knowledge Distillation—0
Agglomeration of Polygonal Grids using Graph Neural Networks with applications to Multigrid solvers—0
Robust Fair Clustering: A Novel Fairness Attack and Defense FrameworkCode0
Fine-tuning Partition-aware Item Similarities for Efficient and Scalable RecommendationCode0
Nimble GNN Embedding with Tensor-Train Decomposition—0
Neural Improvement Heuristics for Graph Combinatorial Optimization ProblemsCode0
Learning to Solve Combinatorial Graph Partitioning Problems via Efficient ExplorationCode1
More Recent Advances in (Hyper)Graph Partitioning—0
Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and LocalizationCode2
Local Motif Clustering via (Hyper)Graph PartitioningCode0
A Bayesian Approach To Graph Partitioning—0
WawPart: Workload-Aware Partitioning of Knowledge Graphs—0
Fast Multi-view Clustering via Ensembles: Towards Scalability, Superiority, and SimplicityCode1
A Differentiable Approach to Combinatorial Optimization using Dataless Neural Networks—0
Generalized Spectral Clustering for Directed and Undirected Graphs—0
SUGAR: Efficient Subgraph-level Training via Resource-aware Graph Partitioning—0
Deep Learning and Spectral Embedding for Graph Partitioning—0
Trading Quality for Efficiency of Graph Partitioning: An Inductive Method across Graphs—0
RAMA: A Rapid Multicut Algorithm on GPUCode1
A Design Flow for Mapping Spiking Neural Networks to Many-Core Neuromorphic Hardware—0
Stateless actor-critic for instance segmentation with high-level priors—0
xER: An Explainable Model for Entity Resolution using an Efficient Solution for the Clique Partitioning Problem—0
_2-norm Flow Diffusion in Near-Linear Time—0
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
Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning and Graph Neural NetworksCode1
Learning Spatial Context with Graph Neural Network for Multi-Person Pose GroupingCode0
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