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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
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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