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Graph Sampling

Training GNNs or generating graph embeddings requires graph samples.

Papers

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Showing 51–100 of 101 papers

TitleStatusHype
GDM: Dual Mixup for Graph Classification with Limited Supervision—0
Impression-Informed Multi-Behavior Recommender System: A Hierarchical Graph Attention Approach—0
STGIN: Spatial-Temporal Graph Interaction Network for Large-scale POI Recommendation—0
A Topology-aware Analysis of Graph Collaborative FilteringCode0
Class-level Structural Relation Modelling and Smoothing for Visual Representation LearningCode0
SynGraphy: Succinct Summarisation of Large Networks via Small Synthetic Representative Graphs—0
MD-GCN: A Multi-Scale Temporal Dual Graph Convolution Network for Traffic Flow Prediction—0
Dynamic Sensor Placement Based on Sampling Theory for Graph Signals—0
Characterizing the Efficiency of Graph Neural Network Frameworks with a Magnifying GlassCode0
Efficient Directed Graph Sampling via Gershgorin Disc Alignment—0
Graph sampling for node embedding—0
Joint Network Topology Inference via a Shared Graphon ModelCode0
Sampling for network function learning—0
Efficient Signed Graph Sampling via Balancing & Gershgorin Disc Perfect Alignment—0
Node Copying: A Random Graph Model for Effective Graph Sampling—0
Fine-tuning Partition-aware Item Similarities for Efficient and Scalable RecommendationCode0
Neuro-Symbolic Procedural Planning with Commonsense Prompting—0
Neighborhood Random Walk Graph Sampling for Regularized Bayesian Graph Convolutional Neural Networks—0
Learning Large-scale Network Embedding from Representative Subgraph—0
Learning Dynamic Preference Structure Embedding From Temporal NetworksCode0
FedGraph: Federated Graph Learning with Intelligent Sampling—0
Gophormer: Ego-Graph Transformer for Node Classification—0
Fast Graph Sampling for Short Video Summarization using Gershgorin Disc Alignment—0
Scaffold-Induced Molecular Graph (SIMG): Effective Graph Sampling Methods for High-Throughput Computational Drug Discovery—0
Are Negative Samples Necessary in Entity Alignment? An Approach with High Performance, Scalability and RobustnessCode0
Bayesian structure learning and sampling of Bayesian networks with the R package BiDAG—0
Graph Sampling Based Deep Metric Learning for Generalizable Person Re-IdentificationCode0
Point Cloud Sampling via Graph Balancing and Gershgorin Disc Alignment—0
Pre-Training on Dynamic Graph Neural NetworksCode0
Learning Parameters for Balanced Index Influence Maximization—0
Text Graph Transformer for Document Classification—0
Accelerating Graph Sampling for Graph Machine Learning using GPUs—0
Little Ball of Fur: A Python Library for Graph SamplingCode0
Reducing Large Internet Topologies for Faster SimulationsCode0
On Random Walk Based Graph SamplingCode0
Folded Graph Signals: Sensing with Unlimited Dynamic Range—0
GraphBGS: Background Subtraction via Recovery of Graph Signals—0
Graph Sampling for Matrix Completion Using Recurrent Gershgorin Disc Shift—0
Influence maximization in unknown social networks: Learning Policies for Effective Graph SamplingCode0
Reconstruction-Cognizant Graph Sampling using Gershgorin Disc Alignment—0
Accurate, Efficient and Scalable Graph EmbeddingCode0
Empirical Risk Minimization and Stochastic Gradient Descent for Relational DataCode0
Counting Motifs with Graph Sampling—0
Adaptive Graph Signal Processing: Algorithms and Optimal Sampling Strategies—0
Unsupervised Learning of Morphology with Graph Sampling—0
A Sampling Theory Perspective of Graph-based Semi-supervised Learning—0
Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction—0
Graph sampling with determinantal processes—0
Adaptive Least Mean Squares Estimation of Graph Signals—0
Evaluating Visual Properties via Robust HodgeRank—0
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