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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 26–50 of 208 papers

TitleStatusHype
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
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
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
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
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