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graph partitioning
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Graph Partitioning is generally the first step of distributed graph computing tasks. The targets are load-balance and minimizing the communication volume.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Demystifying Distributed Training of Graph Neural Networks for Link Prediction
DistMLIP: A Distributed Inference Platform for Machine Learning Interatomic Potentials
Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning
A Graph-Partitioning Based Continuous Optimization Approach to Semi-supervised Clustering Problems
Node-level Contrastive Unlearning on Graph Neural Networks
WaveGAS: Waveform Relaxation for Scaling Graph Neural Networks
Dual-Bounded Nonlinear Optimal Transport for Size Constrained Min Cut Clustering
Hierarchical Superpixel Segmentation via Structural Information Theory
FairGP: A Scalable and Fair Graph Transformer Using Graph Partitioning
Towards the efficacy of federated prediction for epidemics on networks
Attribute-Enhanced Similarity Ranking for Sparse Link Prediction
Graph Neural Networks on Graph Databases
A Random-Key Optimizer for Combinatorial Optimization
Upsampling DINOv2 features for unsupervised vision tasks and weakly supervised materials segmentation
VLSI Hypergraph Partitioning with Deep Learning
Towards Faster Graph Partitioning via Pre-training and Inductive Inference
Revisiting Surgical Instrument Segmentation Without Human Intervention: A Graph Partitioning View
Quantum Annealing based Power Grid Partitioning for Parallel Simulation
LSM-GNN: Large-scale Storage-based Multi-GPU GNN Training by Optimizing Data Transfer Scheme
Distributed Training of Large Graph Neural Networks with Variable Communication Rates
Causal Discovery over High-Dimensional Structured Hypothesis Spaces with Causal Graph Partitioning
FastGAS: Fast Graph-based Annotation Selection for In-Context Learning
A random-key GRASP for combinatorial optimization
A Structure-Aware Framework for Learning Device Placements on Computation Graphs
Exploring Key Point Analysis with Pairwise Generation and Graph Partitioning
CATGNN: Cost-Efficient and Scalable Distributed Training for Graph Neural Networks
A Clustering Method with Graph Maximum Decoding Information
CuVLER: Enhanced Unsupervised Object Discoveries through Exhaustive Self-Supervised Transformers
Unleashing Graph Partitioning for Large-Scale Nearest Neighbor Search
Towards Cohesion-Fairness Harmony: Contrastive Regularization in Individual Fair Graph Clustering
Unsupervised Optimisation of GNNs for Node Clustering
An Effective Branch-and-Bound Algorithm with New Bounding Methods for the Maximum $s$-Bundle Problem
Deep Spectral Improvement for Unsupervised Image Instance Segmentation
Circuit Partitioning for Multi-Core Quantum Architectures with Deep Reinforcement Learning
GLISP: A Scalable GNN Learning System by Exploiting Inherent Structural Properties of Graphs
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