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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 31–40 of 208 papers

TitleStatusHype
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
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