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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

Showing 101125 of 208 papers

TitleStatusHype
Node-level Contrastive Unlearning on Graph Neural Networks0
One-step Bipartite Graph Cut: A Normalized Formulation and Its Application to Scalable Subspace Clustering0
On Hash-Based Work Distribution Methods for Parallel Best-First Search0
On the definition of Shape Parts: a Dominant Sets Approach0
Optimizing embedding-related quantum annealing parameters for reducing hardware bias0
Orientation Robust Text Line Detection in Natural Images0
PolicyClusterGCN: Identifying Efficient Clusters for Training Graph Convolutional Networks0
Preconditioned Spectral Clustering for Stochastic Block Partition Streaming Graph Challenge0
Proximity Preserving Binary Code using Signed Graph-Cut0
Quantum Annealing based Power Grid Partitioning for Parallel Simulation0
RANK: AI-assisted End-to-End Architecture for Detecting Persistent Attacks in Enterprise Networks0
Recent Progress on Graph Partitioning Problems Using Evolutionary Computation0
Recursive Decomposition for Nonconvex Optimization0
Regular Intersection Emptiness of Graph Problems: Finding a Needle in a Haystack of Graphs with the Help of Automata0
Regularized Co-Clustering with Dual Supervision0
Reinforcement learning for instance segmentation with high-level priors0
Relations Between Adjacency and Modularity Graph Partitioning0
Replica Symmetry Breaking in Bipartite Spin Glasses and Neural Networks0
Resolution-limit-free and local Non-negative Matrix Factorization quality functions for graph clustering0
Revisiting Graph Construction for Fast Image Segmentation0
Scalable Graph Convolutional Network Training on Distributed-Memory Systems0
Similarity-Driven Semantic Role Induction via Graph Partitioning0
Spectral Clustering with Imbalanced Data0
Spectral Hashing0
Stateless actor-critic for instance segmentation with high-level priors0
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