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Graph Sampling

Training GNNs or generating graph embeddings requires graph samples.

Papers

Showing 2650 of 101 papers

TitleStatusHype
A Sampling-based Framework for Hypothesis Testing on Large Attributed GraphsCode0
A Topology-aware Analysis of Graph Collaborative FilteringCode0
AGS-GNN: Attribute-guided Sampling for Graph Neural NetworksCode0
Sampling From Large GraphsCode0
Pre-Training on Dynamic Graph Neural NetworksCode0
Cooperative Minibatching in Graph Neural NetworksCode0
On Random Walk Based Graph SamplingCode0
Influence maximization in unknown social networks: Learning Policies for Effective Graph SamplingCode0
Little Ball of Fur: A Python Library for Graph SamplingCode0
Reducing Large Internet Topologies for Faster SimulationsCode0
Hierarchical graph sampling based minibatch learning with chain preservation and variance reductionCode0
Are Negative Samples Necessary in Entity Alignment? An Approach with High Performance, Scalability and RobustnessCode0
Characterizing the Efficiency of Graph Neural Network Frameworks with a Magnifying GlassCode0
Joint Network Topology Inference via a Shared Graphon ModelCode0
Class-level Structural Relation Modelling and Smoothing for Visual Representation LearningCode0
Empirical Risk Minimization and Stochastic Gradient Descent for Relational DataCode0
Accurate, Efficient and Scalable Graph EmbeddingCode0
Graph Sampling Based Deep Metric Learning for Generalizable Person Re-IdentificationCode0
Learning Dynamic Preference Structure Embedding From Temporal NetworksCode0
Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs0
Efficient Directed Graph Sampling via Gershgorin Disc Alignment0
Bearing Fault Diagnosis using Graph Sampling and Aggregation Network0
Edge Sampling of Graphs: Graph Signal Processing Approach With Edge Smoothness0
Dynamic Sensor Placement Based on Sampling Theory for Graph Signals0
Distributed Graph Neural Network Inference With Just-In-Time Compilation For Industry-Scale Graphs0
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