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Graph Sampling

Training GNNs or generating graph embeddings requires graph samples.

Papers

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Showing 51–60 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
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