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Graph Sampling

Training GNNs or generating graph embeddings requires graph samples.

Papers

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Showing 26–50 of 101 papers

TitleStatusHype
Efficient Directed Graph Sampling via Gershgorin Disc Alignment—0
Bearing Fault Diagnosis using Graph Sampling and Aggregation Network—0
Adaptive Least Mean Squares Estimation of Graph Signals—0
Edge Sampling of Graphs: Graph Signal Processing Approach With Edge Smoothness—0
Dynamic Sensor Placement Based on Sampling Theory for Graph Signals—0
Bayesian structure learning and sampling of Bayesian networks with the R package BiDAG—0
Distributed Graph Neural Network Inference With Just-In-Time Compilation For Industry-Scale Graphs—0
GraphBGS: Background Subtraction via Recovery of Graph Signals—0
DiskGNN: Bridging I/O Efficiency and Model Accuracy for Out-of-Core GNN Training—0
Graph sampling with determinantal processes—0
Counting Motifs with Graph Sampling—0
A Sampling Theory Perspective of Graph-based Semi-supervised Learning—0
Graph Sampling for Matrix Completion Using Recurrent Gershgorin Disc Shift—0
GDM: Dual Mixup for Graph Classification with Limited Supervision—0
GCM-Net: Graph-enhanced Cross-Modal Infusion with a Metaheuristic-Driven Network for Video Sentiment and Emotion Analysis—0
Graph sampling for node embedding—0
Folded Graph Signals: Sensing with Unlimited Dynamic Range—0
GLISP: A Scalable GNN Learning System by Exploiting Inherent Structural Properties of Graphs—0
Gophormer: Ego-Graph Transformer for Node Classification—0
FedGraph: Federated Graph Learning with Intelligent Sampling—0
GraphSOS: Graph Sampling and Order Selection to Help LLMs Understand Graphs Better—0
Classification of developmental and brain disorders via graph convolutional aggregation—0
Federated Graph Learning with Adaptive Importance-based Sampling—0
Fast Graph Sampling for Short Video Summarization using Gershgorin Disc Alignment—0
A Poincaré Inequality and Consistency Results for Signal Sampling on Large Graphs—0
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