Graph Convolutional Neural Network

Graph Convolutional Neural Networks (GCNs) are a type of deep learning model designed to analyze data represented as graphs, leveraging the relationships between data points to improve learning. Current research focuses on addressing challenges like over-smoothing (where node representations become too similar), improving efficiency (especially for large graphs), and enhancing fairness and robustness. GCNs are proving valuable across diverse fields, including credit risk assessment, climate modeling, and medical image analysis, by enabling more accurate and efficient predictions and analyses than traditional methods.

Papers

November 6, 2024

Increasing the scalability of graph convolution for FPGA-implemented event-based vision
Piotr Wzorek, Kamil Jeziorek, Tomasz Kryjak, Andrea Pinna
Event Camera Potential Scalability Graph Convolution Graph Convolutional Neural Network Event Based Vision Frame Based FPGA Resource

November 4, 2024

High-Pass Graph Convolutional Network for Enhanced Anomaly Detection: A Novel Approach
Shelei Li, Yong Chai Tan, Tai Vincent
Graph Neural Network Anomaly Detection Novel Approach Graph Convolutional Network Graph Convolutional Neural Network Graph Anomaly Detection Spectral Graph Convolution

October 5, 2024

Applying Hybrid Graph Neural Networks to Strengthen Credit Risk Analysis
Mengfang Sun, Wenying Sun, Ying Sun, Shaobo Liu, Mohan Jiang, Zhen Xu
Risk Prediction Graph Convolutional Neural Network Hybrid Neural Network Credit Risk Credit Decision Credit Scoring Model

September 19, 2024

Graph Convolutional Neural Networks as Surrogate Models for Climate Simulation
Kevin Potter, Carianne Martinez, Reina Pradhan, Samantha Brozak, Steven Sleder, Lauren Wheeler
Surrogate Model Graph Convolutional Neural Network Earth System Earth System Model Climate Simulation

August 6, 2024

TSC: A Simple Two-Sided Constraint against Over-Smoothing
Furong Peng, Kang Liu, Xuan Lu, Yuhua Qian, Hongren Yan, Chao Ma
Graph Convolutional Network Node Representation Deeper Network Graph Convolutional Neural Network Undisciplined Over Smoothing Order Neighbor

June 24, 2024

Feature Fusion for Human Activity Recognition using Parameter-Optimized Multi-Stage Graph Convolutional Network and Transformer Models
Mohammad Belal, Taimur Hassan, Abdelfatah Ahmed, Ahmad Aljarah, Nael Alsheikh, Irfan Hussain
Convolutional Neural Network Transformer Model Activity Recognition Human Motion Feature Fusion Graph Convolutional Neural Network

June 11, 2024

Embedded Graph Convolutional Networks for Real-Time Event Data Processing on SoC FPGAs
Kamil Jeziorek, Piotr Wzorek, Krzysztof Blachut, Andrea Pinna, Tomasz Kryjak
Event Camera Graph Convolutional Network Graph Convolutional Neural Network GCN Based SoC FPGA

June 8, 2024

Transfer Entropy in Graph Convolutional Neural Networks
Adrian Moldovan, Angel Caţaron, Răzvan Andonie
Convolutional Neural Network Graph Neural Network Graph Convolutional Neural Network Multiple Graph GCN Model Transfer Entropy

May 26, 2024

Transfer Learning Under High-Dimensional Graph Convolutional Regression Model for Node Classification
Jiachen Chen, Danyang Huang, Liyuan Wang, Kathryn L. Lunetta, Debarghya Mukherjee, Huimin Cheng
Transfer Learning Node Classification Graph Convolutional Network Graph Convolutional Neural Network

March 10, 2024

February 29, 2024

Graph Convolutional Neural Networks for Automated Echocardiography View Recognition: A Holistic Approach
Sarina Thomas, Cristiana Tiago, Børge Solli Andreassen, Svein Arne Aase, Jurica Šprem, Erik Steen, Anne Solberg, Guy Ben-Yosef
Graph Convolutional Neural Network Holistic Approach Motion Tracking 3d Image Structure Detection View Classification Synthetic Ultrasound

February 23, 2024

Sampling-based Distributed Training with Message Passing Neural Network
Priyesh Kakka, Sheel Nidhan, Rishikesh Ranade, Jonathan F. MacArt
Training Data Graph Convolutional Neural Network Message Passing Neural Network GCN Based

February 14, 2024

Multi-Hierarchical Surrogate Learning for Structural Dynamical Crash Simulations Using Graph Convolutional Neural Networks
Jonas Kneifl, Jörg Fehr, Steven L. Brunton, J. Nathan Kutz
Surrogate Model Graph Convolutional Neural Network Data Driven Surrogate Crash Simulation

February 11, 2024

KGroot: Enhancing Root Cause Analysis through Knowledge Graphs and Graph Convolutional Neural Networks
Tingting Wang, Guilin Qi, Tianxing Wu
Knowledge Graph Fault Detection Root Cause Graph Convolutional Neural Network Post Fault Trajectory Fault Localization SHUSS Root Exploration Term

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 26, 2024

FairSample: Training Fair and Accurate Graph Convolutional Neural Networks Efficiently
Zicun Cong, Shi Baoxu, Shan Li, Jaewon Yang, Qi He, Jian Pei
Model Fairness Graph Convolutional Neural Network Fairness Property Unbiased Segmentation Fair Training

December 20, 2023

Stability of Graph Convolutional Neural Networks through the lens of small perturbation analysis
Lucia Testa, Claudio Battiloro, Stefania Sardellitti, Sergio Barbarossa
Core Stability Camera Lens Graph Convolutional Neural Network Small Perturbation Eigen Portfolio Random Perturbation Laplacian Matrix

December 17, 2023

DER-GCN: Dialogue and Event Relation-Aware Graph Convolutional Neural Network for Multimodal Dialogue Emotion Recognition
Wei Ai, Yuntao Shou, Tao Meng, Nan Yin, Keqin Li
Emotion Recognition Relation Extraction Dialogue Utterance Event Description Multimodal Emotion Recognition Graph Convolutional Neural Network C2p GCN Dialogue Emotion

November 10, 2023

Reviewing Developments of Graph Convolutional Network Techniques for Recommendation Systems
Haojun Zhu, Vikram Kapoor, Priya Sharma
Graph Drawing Recommender System Recommendation System Graph Convolutional Neural Network Graph Construction

Graph Convolutional Neural Network

Papers

Increasing the scalability of graph convolution for FPGA-implemented event-based vision

High-Pass Graph Convolutional Network for Enhanced Anomaly Detection: A Novel Approach

Applying Hybrid Graph Neural Networks to Strengthen Credit Risk Analysis

Graph Convolutional Neural Networks as Surrogate Models for Climate Simulation

TSC: A Simple Two-Sided Constraint against Over-Smoothing

Feature Fusion for Human Activity Recognition using Parameter-Optimized Multi-Stage Graph Convolutional Network and Transformer Models

Embedded Graph Convolutional Networks for Real-Time Event Data Processing on SoC FPGAs

Transfer Entropy in Graph Convolutional Neural Networks

Transfer Learning Under High-Dimensional Graph Convolutional Regression Model for Node Classification

Attention is all you need for boosting graph convolutional neural network

L^2GC:Lorentzian Linear Graph Convolutional Networks for Node Classification

Graph Convolutional Neural Networks for Automated Echocardiography View Recognition: A Holistic Approach

Sampling-based Distributed Training with Message Passing Neural Network

Multi-Hierarchical Surrogate Learning for Structural Dynamical Crash Simulations Using Graph Convolutional Neural Networks

KGroot: Enhancing Root Cause Analysis through Knowledge Graphs and Graph Convolutional Neural Networks

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

FairSample: Training Fair and Accurate Graph Convolutional Neural Networks Efficiently

Stability of Graph Convolutional Neural Networks through the lens of small perturbation analysis

DER-GCN: Dialogue and Event Relation-Aware Graph Convolutional Neural Network for Multimodal Dialogue Emotion Recognition

Reviewing Developments of Graph Convolutional Network Techniques for Recommendation Systems