Graph Convolutional Network

Graph Convolutional Networks (GCNs) are a type of neural network designed to analyze data represented as graphs, focusing on learning relationships between interconnected nodes. Current research emphasizes improving GCN performance through techniques like graph pruning, transfer learning, and incorporating diverse data modalities (e.g., multi-omics, spatio-temporal data) into model architectures such as variational mode decomposition and dual graph convolutional networks. GCNs find broad application in diverse fields, including traffic prediction, drug response prediction, and disease diagnosis, offering powerful tools for analyzing complex relational data and extracting meaningful insights.

Papers

May 19, 2024

A Comparative Analysis of Student Performance Predictions in Online Courses using Heterogeneous Knowledge Graphs
Thomas Trask, Dr. Nicholas Lytle, Michael Boyle, Dr. David Joyner, Dr. Ahmed Mubarak
Comparative Study Graph Convolutional Network Student Performance Prediction Massive Open Online Course Student Performance Online Training Heterogeneous Knowledge Graph

May 5, 2024

Convolutional Learning on Directed Acyclic Graphs
Samuel Rey, Hamed Ajorlou, Gonzalo Mateos
Deep Convolutional Neural Network Graph Convolutional Network Directed Acyclic Graph DAG Learning Asymmetry Labeled DAG

April 30, 2024

Bayesian Functional Connectivity and Graph Convolutional Network for Working Memory Load Classification
Harshini Gangapuram, Vidya Manian
Graph Convolutional Network Functional Connectivity Cognitive Load Brain Dynamic

April 24, 2024

April 19, 2024

Graph Convolutional Network For Semi-supervised Node Classification With Subgraph Sketching
Zibin Huang, Jun Xian
Graph Convolutional Network Semi Supervised Node Classification Deep Graph Clustering

April 15, 2024

RandAlign: A Parameter-Free Method for Regularizing Graph Convolutional Networks
Haimin Zhang, Min Xu
Convolutional Neural Network Graph Representation Learning Graph Convolutional Network Graph Convolution Convolutional Neural Network Model

April 9, 2024

Integrative Deep Learning Framework for Parkinson's Disease Early Detection using Gait Cycle Data Measured by Wearable Sensors: A CNN-GRU-GNN Approach
Alireza Rashnu, Armin Salimi-Badr
Graph Neural Network Graph Convolutional Network Wearable Sensor Gated Recurrent Unit Early Disease Detection 1D Convolutional Neural Network

April 3, 2024

Multi-Scale Spatial-Temporal Self-Attention Graph Convolutional Networks for Skeleton-based Action Recognition
Ikuo Nakamura
Self Attention Graph Convolutional Network Skeleton Based Action Recognition Spatial Self Attention

March 26, 2024

Integrative Graph-Transformer Framework for Histopathology Whole Slide Image Representation and Classification
Zhan Shi, Jingwei Zhang, Jun Kong, Fusheng Wang
Classification Code Graph Transformer Graph Convolutional Network Digital Pathology Global Attention

March 24, 2024

Node Classification via Semantic-Structural Attention-Enhanced Graph Convolutional Networks
Hongyin Zhu
Deep Convolutional Neural Network Node Classification Graph Convolutional Network Graph Based Network Datasets

March 23, 2024

STEntConv: Predicting Disagreement with Stance Detection and a Signed Graph Convolutional Network
Isabelle Lorge, Li Zhang, Xiaowen Dong, Janet B. Pierrehumbert
Graph Convolutional Network Stance Detection Online Discourse Stance Analysis Opinion Disagreement disAgreement Score

March 16, 2024

FlyKD: Graph Knowledge Distillation on the Fly with Curriculum Learning
Eugene Ku
Knowledge Distillation Pseudo Label Curriculum Learning Graph Convolutional Network Noisy Pseudo Label Graph Knowledge

March 8, 2024

C2P-GCN: Cell-to-Patch Graph Convolutional Network for Colorectal Cancer Grading
Sudipta Paul, Bulent Yener, Amanda W. Lund
Graph Convolutional Network Colorectal Cancer C2p GCN Cell Graph Image to Graph

March 6, 2024

Self-Attention Empowered Graph Convolutional Network for Structure Learning and Node Embedding
Mengying Jiang, Guizhong Liu, Yuanchao Su, Xinliang Wu
Deep Convolutional Neural Network Graph Learning Graph Convolutional Network Node Embeddings Graph Structure Learning Structure Learning

February 25, 2024

Deep Contrastive Graph Learning with Clustering-Oriented Guidance
Mulin Chen, Bocheng Wang, Xuelong Li
Contrastive Learning Graph Convolutional Network Graph Structure Learning Contrastive Graph Graph Based Clustering Fine Grained Personalized Guidance

January 31, 2024

IGCN: Integrative Graph Convolution Networks for patient level insights and biomarker discovery in multi-omics integration
Cagri Ozdemir, Mohammad Al Olaimat, Yashu Vashishath, Serdar Bozdag, Alzheimer's Disease Neuroimaging Initiative
Graph Neural Network Graph Convolutional Network Node Embeddings Multi Modal Data Graph Convolutional Neural Network Multiple Embeddings

January 16, 2024

MA2GCN: Multi Adjacency relationship Attention Graph Convolutional Networks for Traffic Prediction using Trajectory data
Zhengke Sun, Yuliang Ma
Graph Convolutional Network Trajectory Data GPS Trajectory Future Traffic Vehicle Trajectory Data Traffic Congestion Prediction

January 12, 2024

Improving Graph Convolutional Networks with Transformer Layer in social-based items recommendation
Thi Linh Hoang, Tuan Dung Pham, Viet Cuong Ta
Transformer Architecture Link Prediction Graph Convolutional Network Social Network Transformer Layer Encoder Architecture

December 29, 2023

One-Shot Multi-Rate Pruning of Graph Convolutional Networks
Hichem Sahbi
Neural Network Graph Convolutional Network Edge Pruning Magnitude Pruning High Pruning Regime

Graph Convolutional Network

Papers

A Comparative Analysis of Student Performance Predictions in Online Courses using Heterogeneous Knowledge Graphs

Convolutional Learning on Directed Acyclic Graphs

Bayesian Functional Connectivity and Graph Convolutional Network for Working Memory Load Classification

Improving Multi-label Recognition using Class Co-Occurrence Probabilities

3D Human Pose Estimation with Occlusions: Introducing BlendMimic3D Dataset and GCN Refinement

Graph Convolutional Network For Semi-supervised Node Classification With Subgraph Sketching

RandAlign: A Parameter-Free Method for Regularizing Graph Convolutional Networks

Integrative Deep Learning Framework for Parkinson's Disease Early Detection using Gait Cycle Data Measured by Wearable Sensors: A CNN-GRU-GNN Approach

Multi-Scale Spatial-Temporal Self-Attention Graph Convolutional Networks for Skeleton-based Action Recognition

Integrative Graph-Transformer Framework for Histopathology Whole Slide Image Representation and Classification

Node Classification via Semantic-Structural Attention-Enhanced Graph Convolutional Networks

STEntConv: Predicting Disagreement with Stance Detection and a Signed Graph Convolutional Network

FlyKD: Graph Knowledge Distillation on the Fly with Curriculum Learning

C2P-GCN: Cell-to-Patch Graph Convolutional Network for Colorectal Cancer Grading

Self-Attention Empowered Graph Convolutional Network for Structure Learning and Node Embedding

Deep Contrastive Graph Learning with Clustering-Oriented Guidance

IGCN: Integrative Graph Convolution Networks for patient level insights and biomarker discovery in multi-omics integration

MA2GCN: Multi Adjacency relationship Attention Graph Convolutional Networks for Traffic Prediction using Trajectory data

Improving Graph Convolutional Networks with Transformer Layer in social-based items recommendation

One-Shot Multi-Rate Pruning of Graph Convolutional Networks