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Graph Sampling
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Training GNNs or generating graph embeddings requires graph samples.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Simple yet Effective Graph Distillation via Clustering
Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs
The Limits of Graph Samplers for Training Inductive Recommender Systems: Extended results
Graph Learning at Scale: Characterizing and Optimizing Pre-Propagation GNNs
Distributed Graph Neural Network Inference With Just-In-Time Compilation For Industry-Scale Graphs
Hierarchical graph sampling based minibatch learning with chain preservation and variance reduction
GraphSOS: Graph Sampling and Order Selection to Help LLMs Understand Graphs Better
BMG-Q: Localized Bipartite Match Graph Attention Q-Learning for Ride-Pooling Order Dispatch
Physics-Guided Fair Graph Sampling for Water Temperature Prediction in River Networks
Towards joint graph learning and sampling set selection from data
Graph Sampling for Scalable and Expressive Graph Neural Networks on Homophilic Graphs
GCM-Net: Graph-enhanced Cross-Modal Infusion with a Metaheuristic-Driven Network for Video Sentiment and Emotion Analysis
Federated Graph Learning with Adaptive Importance-based Sampling
Retrofitting Temporal Graph Neural Networks with Transformer
Bearing Fault Diagnosis using Graph Sampling and Aggregation Network
Unveiling Privacy Vulnerabilities: Investigating the Role of Structure in Graph Data
Edge Sampling of Graphs: Graph Signal Processing Approach With Edge Smoothness
Light-weight End-to-End Graph Interest Network for CTR Prediction in E-commerce Search
AGS-GNN: Attribute-guided Sampling for Graph Neural Networks
DiskGNN: Bridging I/O Efficiency and Model Accuracy for Out-of-Core GNN Training
A Sampling-based Framework for Hypothesis Testing on Large Attributed Graphs
Accelerating Scientific Discovery with Generative Knowledge Extraction, Graph-Based Representation, and Multimodal Intelligent Graph Reasoning
GLISP: A Scalable GNN Learning System by Exploiting Inherent Structural Properties of Graphs
A Poincaré Inequality and Consistency Results for Signal Sampling on Large Graphs
Classification of developmental and brain disorders via graph convolutional aggregation
Interpretable A-posteriori Error Indication for Graph Neural Network Surrogate Models
Cooperative Minibatching in Graph Neural Networks
Causal structure learning with momentum: Sampling distributions over Markov Equivalence Classes of DAGs
GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks
Graph Neural Network for Stress Predictions in Stiffened Panels Under Uniform Loading
GDM: Dual Mixup for Graph Classification with Limited Supervision
Impression-Informed Multi-Behavior Recommender System: A Hierarchical Graph Attention Approach
STGIN: Spatial-Temporal Graph Interaction Network for Large-scale POI Recommendation
A Topology-aware Analysis of Graph Collaborative Filtering
Class-level Structural Relation Modelling and Smoothing for Visual Representation Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.