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Graph Sampling

Training GNNs or generating graph embeddings requires graph samples.

Papers

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Showing 51–75 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
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