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, 2023

Barron Space for Graph Convolution Neural Networks
Seok-Young Chung, Qiyu Sun
Kernel Hilbert Space Graph Convolutional Neural Network Graph Domain Neural Kernel Kernel Banach Space Barron Space

October 14, 2023

Graph Neural Network approaches for single-cell data: A recent overview
Konstantinos Lazaros, Dimitris E. Koumadorakis, Panagiotis Vlamos, Aristidis G. Vrahatis
Graph Neural Network Graph Convolutional Neural Network Recent Development Single Cell Data Cell Annotation

October 11, 2023

GRaMuFeN: Graph-based Multi-modal Fake News Detection in Social Media
Makan Kananian, Fatima Badiei, S. AmirAli Gh. Ghahramani
Convolutional Neural Network Social Medium Fake News Convolutional Neural Network Model Graph Convolutional Neural Network Fake Speech Step Gram

August 23, 2023

Predicting Drug Solubility Using Different Machine Learning Methods -- Linear Regression Model with Extracted Chemical Features vs Graph Convolutional Neural Network
John Ho, Zhao-Heng Yin, Colin Zhang, Nicole Guo, Yang Ha
Machine Learning Known Molecule Regression Model Graph Convolutional Neural Network Chemical Structure Solubility Prediction

July 24, 2023

Compact & Capable: Harnessing Graph Neural Networks and Edge Convolution for Medical Image Classification
Aryan Singh, Pepijn Van de Ven, Ciarán Eising, Patrick Denny
Graph Neural Network Graph Attention Network Medical Image Classification Graph Convolutional Neural Network Embracing CompAct Graph Based Neural Graph Auto Edge Convolution

July 3, 2023

Node-weighted Graph Convolutional Network for Depression Detection in Transcribed Clinical Interviews
Sergio Burdisso, Esaú Villatoro-Tello, Srikanth Madikeri, Petr Motlicek
Graph Convolutional Network Depression Detection Graph Convolutional Neural Network Manipulation Edge Detection Module Clinical Interview

June 25, 2023

PolicyClusterGCN: Identifying Efficient Clusters for Training Graph Convolutional Networks
Saket Gurukar, Shaileshh Bojja Venkatakrishnan, Balaraman Ravindran, Srinivasan Parthasarathy
Convolutional Neural Network Policy Gradient Graph Convolutional Neural Network Policy Network

June 7, 2023

Robust-DefReg: A Robust Deformable Point Cloud Registration Method based on Graph Convolutional Neural Networks
Sara Monji-Azad, Marvin Kinz, Jürgen Hesser
Point Cloud Registration Graph Convolutional Neural Network Non Rigid Registration Non Rigid Point Cloud Coarse to finE Registration

May 30, 2023

Budget-Aware Graph Convolutional Network Design using Probabilistic Magnitude Pruning
Hichem Sahbi
Convolutional Neural Network Graph Convolutional Network Graph Convolutional Neural Network Magnitude Pruning

May 29, 2023

Fourier Analysis on Robustness of Graph Convolutional Neural Networks for Skeleton-based Action Recognition
Nariki Tanaka, Hiroshi Kera, Kazuhiko Kawamoto
Adversarial Attack Native Robustness Adversarial Training Skeleton Based Action Recognition Graph Convolutional Neural Network Fourier Analysis Graph Fourier

April 24, 2023

Graph Convolutional Networks based on Manifold Learning for Semi-Supervised Image Classification
Lucas Pascotti Valem, Daniel Carlos Guimarães Pedronette, Longin Jan Latecki
Manifold Learning Graph Convolutional Neural Network Semi Supervised Image Classification

March 31, 2023

MLGCN: An Ultra Efficient Graph Convolution Neural Model For 3D Point Cloud Analysis
Mohammad Khodadad, Morteza Rezanejad, Ali Shiraee Kasmaee, Kaleem Siddiqi, Dirk Walther, Hamidreza Mahyar
Graph Neural Network Point Cloud 3D Point Cloud Point Cloud Analysis Graph Convolutional Neural Network Shape Analysis

March 16, 2023

AU-aware graph convolutional network for Macro- and Micro-expression spotting
Shukang Yin, Shiwei Wu, Tong Xu, Shifeng Liu, Sirui Zhao, Enhong Chen
Graph Drawing Deep Convolutional Neural Network Human Face Graph Convolutional Neural Network Micro Expression Global Scale Convolution Based

February 16, 2023

Robust Mid-Pass Filtering Graph Convolutional Networks
Jincheng Huang, Lun Du, Xu Chen, Qiang Fu, Shi Han, Dongmei Zhang
Adversarial Attack Graph Convolutional Neural Network GCN Based

February 1, 2023

Simple yet Effective Gradient-Free Graph Convolutional Networks
Yulin Zhu, Xing Ai, Qimai Li, Xiao-Ming Wu, Kai Zhou
Graph Neural Network Graph Representation Learning Graph Convolutional Neural Network

January 9, 2023

Few-shot Semantic Segmentation with Support-induced Graph Convolutional Network
Jie Liu, Yanqi Bao, Wenzhe Yin, Haochen Wang, Yang Gao, Jan-Jakob Sonke, Efstratios Gavves
Graph Convolutional Network Graph Convolutional Neural Network Shot Semantic Segmentation Salient Object Latent Structure Aware Query

December 26, 2022

Homophily modulates double descent generalization in graph convolution networks
Cheng Shi, Liming Pan, Hong Hu, Ivan Dokmanić
Strong Generalization Graph Convolutional Neural Network Double Descent Graph Convolution Network Noisy Graph Homophily Level

December 21, 2022

Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Neural Networks
Sitao Luan, Mingde Zhao, Chenqing Hua, Xiao-Wen Chang, Doina Precup
Graph Neural Network Node Classification Node Representation GNN Model Graph Convolutional Neural Network Aggregation Function Expert Diversification GNN Method Missing Half

December 5, 2022

Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point Processes
Wei Duan, Junyu Xuan, Maoying Qiao, Jie Lu
Convolutional Neural Network Graph Representation Learning Graph Convolution Node Representation Negative Sample Graph Convolutional Neural Network Determinantal Point Process Graph Structure Information

December 1, 2022

Graph Convolutional Neural Networks as Parametric CoKleisli morphisms
Bruno Gavranović, Mattia Villani
Gene Level GNN Graph Convolutional Neural Network Categorical Structure

Graph Convolutional Neural Network

Papers

Barron Space for Graph Convolution Neural Networks

Graph Neural Network approaches for single-cell data: A recent overview

GRaMuFeN: Graph-based Multi-modal Fake News Detection in Social Media

Predicting Drug Solubility Using Different Machine Learning Methods -- Linear Regression Model with Extracted Chemical Features vs Graph Convolutional Neural Network

Compact & Capable: Harnessing Graph Neural Networks and Edge Convolution for Medical Image Classification

Node-weighted Graph Convolutional Network for Depression Detection in Transcribed Clinical Interviews

PolicyClusterGCN: Identifying Efficient Clusters for Training Graph Convolutional Networks

Robust-DefReg: A Robust Deformable Point Cloud Registration Method based on Graph Convolutional Neural Networks

Budget-Aware Graph Convolutional Network Design using Probabilistic Magnitude Pruning

Fourier Analysis on Robustness of Graph Convolutional Neural Networks for Skeleton-based Action Recognition

Graph Convolutional Networks based on Manifold Learning for Semi-Supervised Image Classification

MLGCN: An Ultra Efficient Graph Convolution Neural Model For 3D Point Cloud Analysis

AU-aware graph convolutional network for Macro- and Micro-expression spotting

Robust Mid-Pass Filtering Graph Convolutional Networks

Simple yet Effective Gradient-Free Graph Convolutional Networks

Few-shot Semantic Segmentation with Support-induced Graph Convolutional Network

Homophily modulates double descent generalization in graph convolution networks

Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Neural Networks

Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point Processes

Graph Convolutional Neural Networks as Parametric CoKleisli morphisms