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Graph Neural Network

Papers

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Showing 951–975 of 3598 papers

TitleStatusHype
Frequency Analysis for Graph Convolution Network—0
Sparse-Dyn: Sparse Dynamic Graph Multi-representation Learning via Event-based Sparse Temporal Attention Network—0
Efficient Graph Neural Network Inference at Large Scale—0
Dynamic Graph Neural Network for Traffic Forecasting in Wide Area Networks—0
Dynamic graph neural network for fake news detection—0
Building a Graph-based Deep Learning network model from captured traffic traces—0
Efficient Learning on Large Graphs using a Densifying Regularity Lemma—0
Efficient Long-distance Latent Relation-aware Graph Neural Network for Multi-modal Emotion Recognition in Conversations—0
DynamicGlue: Epipolar and Time-Informed Data Association in Dynamic Environments using Graph Neural Networks—0
Advanced RAG Models with Graph Structures: Optimizing Complex Knowledge Reasoning and Text Generation—0
Enhancing Worker Recruitment in Collaborative Mobile Crowdsourcing: A Graph Neural Network Trust Evaluation Approach—0
Efficient Traffic State Forecasting using Spatio-Temporal Network Dependencies: A Sparse Graph Neural Network Approach—0
Dynamic Edge Weights in Graph Neural Networks for 3D Object Detection—0
Dynamic Collaborative Multi-Agent Reinforcement Learning Communication for Autonomous Drone Reforestation—0
BS-GAT Behavior Similarity Based Graph Attention Network for Network Intrusion Detection—0
Dynamically Local-Enhancement Planner for Large-Scale Autonomous Driving—0
Bridging the Semantic-Numerical Gap: A Numerical Reasoning Method of Cross-modal Knowledge Graph for Material Property Prediction—0
CDFGNN: a Systematic Design of Cache-based Distributed Full-Batch Graph Neural Network Training with Communication Reduction—0
Embedding-based Multimodal Learning on Pan-Squamous Cell Carcinomas for Improved Survival Outcomes—0
Embedding Symbolic Temporal Knowledge into Deep Sequential Models—0
Frequency Feature Fusion Graph Network For Depression Diagnosis Via fNIRS—0
From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale Contrastive Learning Approach—0
Empowering Graph Representation Learning with Paired Training and Graph Co-Attention—0
GraphSCENE: On-Demand Critical Scenario Generation for Autonomous Vehicles in Simulation—0
GAIN: Graph Attention & Interaction Network for Inductive Semi-Supervised Learning over Large-scale Graphs—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1air0S1—Unverified