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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
Fine-tuning Partition-aware Item Similarities for Efficient and Scalable RecommendationCode0
A Topology-aware Analysis of Graph Collaborative FilteringCode0
A Sampling-based Framework for Hypothesis Testing on Large Attributed GraphsCode0
Cooperative Minibatching in Graph Neural NetworksCode0
Class-level Structural Relation Modelling and Smoothing for Visual Representation LearningCode0
Pre-Training on Dynamic Graph Neural NetworksCode0
Joint Network Topology Inference via a Shared Graphon ModelCode0
Characterizing the Efficiency of Graph Neural Network Frameworks with a Magnifying GlassCode0
Hierarchical graph sampling based minibatch learning with chain preservation and variance reductionCode0
Learning Dynamic Preference Structure Embedding From Temporal NetworksCode0
Empirical Risk Minimization and Stochastic Gradient Descent for Relational DataCode0
Are Negative Samples Necessary in Entity Alignment? An Approach with High Performance, Scalability and RobustnessCode0
Accurate, Efficient and Scalable Graph EmbeddingCode0
Influence maximization in unknown social networks: Learning Policies for Effective Graph SamplingCode0
Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs—0
Efficient Directed Graph Sampling via Gershgorin Disc Alignment—0
Bearing Fault Diagnosis using Graph Sampling and Aggregation Network—0
Edge Sampling of Graphs: Graph Signal Processing Approach With Edge Smoothness—0
Dynamic Sensor Placement Based on Sampling Theory for Graph Signals—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
Bayesian structure learning and sampling of Bayesian networks with the R package BiDAG—0
Adaptive Least Mean Squares Estimation of Graph Signals—0
GraphSOS: Graph Sampling and Order Selection to Help LLMs Understand Graphs Better—0
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