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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 51–75 of 208 papers

TitleStatusHype
Intel nGraph: An Intermediate Representation, Compiler, and Executor for Deep LearningCode0
A random-key GRASP for combinatorial optimizationCode0
Graph-Partitioning-Based Diffusion Convolutional Recurrent Neural Network for Large-Scale Traffic ForecastingCode0
The Product CutCode0
Hierarchical Superpixel Segmentation via Structural Information TheoryCode0
Learning Space Partitions for Nearest Neighbor SearchCode0
BClean: A Bayesian Data Cleaning SystemCode0
Towards the efficacy of federated prediction for epidemics on networksCode0
Generalizable Resource Allocation in Stream Processing via Deep Reinforcement LearningCode0
DistDGL: Distributed Graph Neural Network Training for Billion-Scale GraphsCode0
GAP: Generalizable Approximate Graph Partitioning FrameworkCode0
A Min-max Cult Algorithm for Graph Partitioning and Data ClusteringCode0
FairGP: A Scalable and Fair Graph Transformer Using Graph PartitioningCode0
Exploring Key Point Analysis with Pairwise Generation and Graph PartitioningCode0
Distributed Graph Embedding with Information-Oriented Random WalksCode0
Distributed Evolutionary Graph PartitioningCode0
Ego-splitting Framework: from Non-Overlapping to Overlapping ClustersCode0
Federated Classification in Hyperbolic Spaces via Secure Aggregation of Convex HullsCode0
Robust Fair Clustering: A Novel Fairness Attack and Defense FrameworkCode0
Distributed Compressed Sparse Row Format for Spiking Neural Network Simulation, Serialization, and Interoperability—0
DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks—0
Attribute-Enhanced Similarity Ranking for Sparse Link Prediction—0
DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks—0
Associating Inter-Image Salient Instances for Weakly Supervised Semantic Segmentation—0
Algorithms for metric learning via contrastive embeddings—0
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