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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 1–50 of 208 papers

TitleStatusHype
PGT-I: Scaling Spatiotemporal GNNs with Memory-Efficient Distributed TrainingCode0
Demystifying Distributed Training of Graph Neural Networks for Link PredictionCode0
DistMLIP: A Distributed Inference Platform for Machine Learning Interatomic PotentialsCode2
Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning—0
A Graph-Partitioning Based Continuous Optimization Approach to Semi-supervised Clustering Problems—0
Node-level Contrastive Unlearning on Graph Neural Networks—0
WaveGAS: Waveform Relaxation for Scaling Graph Neural Networks—0
Dual-Bounded Nonlinear Optimal Transport for Size Constrained Min Cut Clustering—0
Hierarchical Superpixel Segmentation via Structural Information TheoryCode0
FairGP: A Scalable and Fair Graph Transformer Using Graph PartitioningCode0
Towards the efficacy of federated prediction for epidemics on networksCode0
Attribute-Enhanced Similarity Ranking for Sparse Link Prediction—0
Graph Neural Networks on Graph Databases—0
A Random-Key Optimizer for Combinatorial Optimization—0
Upsampling DINOv2 features for unsupervised vision tasks and weakly supervised materials segmentationCode1
VLSI Hypergraph Partitioning with Deep Learning—0
Towards Faster Graph Partitioning via Pre-training and Inductive InferenceCode0
Revisiting Surgical Instrument Segmentation Without Human Intervention: A Graph Partitioning ViewCode0
Quantum Annealing based Power Grid Partitioning for Parallel Simulation—0
LSM-GNN: Large-scale Storage-based Multi-GPU GNN Training by Optimizing Data Transfer Scheme—0
Distributed Training of Large Graph Neural Networks with Variable Communication Rates—0
Causal Discovery over High-Dimensional Structured Hypothesis Spaces with Causal Graph PartitioningCode0
FastGAS: Fast Graph-based Annotation Selection for In-Context Learning—0
A random-key GRASP for combinatorial optimizationCode0
A Structure-Aware Framework for Learning Device Placements on Computation GraphsCode1
Exploring Key Point Analysis with Pairwise Generation and Graph PartitioningCode0
CATGNN: Cost-Efficient and Scalable Distributed Training for Graph Neural Networks—0
A Clustering Method with Graph Maximum Decoding Information—0
CuVLER: Enhanced Unsupervised Object Discoveries through Exhaustive Self-Supervised TransformersCode1
Unleashing Graph Partitioning for Large-Scale Nearest Neighbor SearchCode1
Towards Cohesion-Fairness Harmony: Contrastive Regularization in Individual Fair Graph ClusteringCode0
Unsupervised Optimisation of GNNs for Node Clustering—0
An Effective Branch-and-Bound Algorithm with New Bounding Methods for the Maximum s-Bundle Problem—0
Deep Spectral Improvement for Unsupervised Image Instance SegmentationCode0
Circuit Partitioning for Multi-Core Quantum Architectures with Deep Reinforcement Learning—0
GLISP: A Scalable GNN Learning System by Exploiting Inherent Structural Properties of Graphs—0
Fine-Grained Bipartite Concept Factorization for Clustering—0
Large Scale Training of Graph Neural Networks for Optimal Markov-Chain Partitioning Using the Kemeny Constant—0
Uplifting the Expressive Power of Graph Neural Networks through Graph Partitioning—0
A Novel Differentiable Loss Function for Unsupervised Graph Neural Networks in Graph Partitioning—0
BClean: A Bayesian Data Cleaning SystemCode0
NeuroCUT: A Neural Approach for Robust Graph PartitioningCode0
Mitigating Pilot Contamination and Enabling IoT Scalability in Massive MIMO Systems—0
DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks—0
An Experimental Comparison of Partitioning Strategies for Distributed Graph Neural Network Training—0
Accelerating Generic Graph Neural Networks via Architecture, Compiler, Partition Method Co-Design—0
Federated Classification in Hyperbolic Spaces via Secure Aggregation of Convex HullsCode0
Self-supervised Few-shot Learning for Semantic Segmentation: An Annotation-free ApproachCode1
Spectral Normalized-Cut Graph Partitioning with Fairness ConstraintsCode0
Edge-set reduction to efficiently solve the graph partitioning problem with the genetic algorithm—0
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