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Methods/GCN

GCN

Graph Convolutional Network

GraphsIntroduced 2000969 papers
Source Paper

Description

A Graph Convolutional Network, or GCN, is an approach for semi-supervised learning on graph-structured data. It is based on an efficient variant of convolutional neural networks which operate directly on graphs. The choice of convolutional architecture is motivated via a localized first-order approximation of spectral graph convolutions. The model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes.

Papers Using This Method

Temporal-Aware Graph Attention Network for Cryptocurrency Transaction Fraud Detection2025-06-26Dense Video Captioning using Graph-based Sentence Summarization2025-06-25EHCube4P: Learning Epistatic Patterns Through Hypercube Graph Convolution Neural Network for Protein Fitness Function Estimation2025-06-20GCN-Driven Reinforcement Learning for Probabilistic Real-Time Guarantees in Industrial URLLC2025-06-17ST-GraphNet: A Spatio-Temporal Graph Neural Network for Understanding and Predicting Automated Vehicle Crash Severity2025-06-09Denoising Programming Knowledge Tracing with a Code Graph-based Tuning Adaptor2025-06-07LLM-based Generative Error Correction for Rare Words with Synthetic Data and Phonetic Context2025-05-23Nonparametric Teaching for Graph Property Learners2025-05-20Mixture Policy based Multi-Hop Reasoning over N-tuple Temporal Knowledge Graphs2025-05-19GNN-Suite: a Graph Neural Network Benchmarking Framework for Biomedical Informatics2025-05-15Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation2025-05-12HDiffTG: A Lightweight Hybrid Diffusion-Transformer-GCN Architecture for 3D Human Pose Estimation2025-05-07Robustness questions the interpretability of graph neural networks: what to do?2025-05-053D Human Pose Estimation via Spatial Graph Order Attention and Temporal Body Aware Transformer2025-05-02Combining GCN Structural Learning with LLM Chemical Knowledge for or Enhanced Virtual Screening2025-04-24Simplifying Graph Convolutional Networks with Redundancy-Free Neighbors2025-04-18SkeletonX: Data-Efficient Skeleton-based Action Recognition via Cross-sample Feature Aggregation2025-04-16Detecting Credit Card Fraud via Heterogeneous Graph Neural Networks with Graph Attention2025-04-11DG-STMTL: A Novel Graph Convolutional Network for Multi-Task Spatio-Temporal Traffic Forecasting2025-04-10Squeeze and Excitation: A Weighted Graph Contrastive Learning for Collaborative Filtering2025-04-06