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

April 30, 2023

SLSG: Industrial Image Anomaly Detection by Learning Better Feature Embeddings and One-Class Classification
Minghui Yang, Jing Liu, Zhiwei Yang, Zhaoyang Wu
Anomaly Detection Graph Convolutional Network Feature Embeddings One Class Classification Anomaly Detection Performance Industrial Image Anomaly Detection

April 25, 2023

Spatiotemporal Graph Convolutional Recurrent Neural Network Model for Citywide Air Pollution Forecasting
Van-Duc Le
Graph Convolutional Network Air Quality Air Quality Forecasting

April 17, 2023

Transformer-based Graph Neural Networks for Outfit Generation
Federico Becattini, Federico Maria Teotini, Alberto Del Bimbo
Graph Neural Network Graph Convolutional Network Fashion Recommendation Outfit Generation

April 14, 2023

AGNN: Alternating Graph-Regularized Neural Networks to Alleviate Over-Smoothing
Zhaoliang Chen, Zhihao Wu, Zhenghong Lin, Shiping Wang, Claudia Plant, Wenzhong Guo
Graph Convolutional Network Graph Convolutional Order Graph Smoothing Problem Graph Regularized Neural Network

April 12, 2023

Vehicle Trajectory Prediction based Predictive Collision Risk Assessment for Autonomous Driving in Highway Scenarios
Dejian Meng, Wei Xiao, Lijun Zhang, Zhuang Zhang, Zihao Liu
Autonomous Driving Graph Convolutional Network Vehicle Interaction Vehicle Trajectory Prediction Collision Risk Traffic Rule

April 10, 2023

Missing Data Imputation with Graph Laplacian Pyramid Network
Weiqi Zhang, Guanlve Li, Jianheng Tang, Jia Li, Fugee Tsung
Graph Convolutional Network Imputation Algorithm Data Imputation Knowledge Enhanced Conditional Imputation Imputation Accuracy Missing Data Imputation

April 4, 2023

InfluencerRank: Discovering Effective Influencers via Graph Convolutional Attentive Recurrent Neural Networks
Seungbae Kim, Jyun-Yu Jiang, Jinyoung Han, Wei Wang
Graph Convolutional Network Influence Score

March 31, 2023

HD-GCN:A Hybrid Diffusion Graph Convolutional Network
Zhi Yang, Kang Li, Haitao Gan, Zhongwei Huang, Ming Shi
Graph Convolutional Network Graph Convolution Information Diffusion GCN Model Quater GCN Diffusion Graph Convolutional Network

March 29, 2023

GRAF: Graph Attention-aware Fusion Networks
Ziynet Nesibe Kesimoglu, Serdar Bozdag
Graph Neural Network Graph Convolutional Network Dense Network Graph Fused Attention Network

March 7, 2023

Stock Price Prediction Using Temporal Graph Model with Value Chain Data
Chang Liu, Sandra Paterlini
Graph Convolutional Network Temporal Graph Stock Price Prediction Stock Return Prediction Value Chain

March 2, 2023

Attention-based Graph Convolution Fusing Latent Structures and Multiple Features for Graph Neural Networks
Yang Li, Yuichi Tanaka
Graph Neural Network Graph Convolutional Network Graph Convolution Node Feature Multiple Feature

February 22, 2023

Graph Construction using Principal Axis Trees for Simple Graph Convolution
Mashaan Alshammari, John Stavrakakis, Adel F. Ahmed, Masahiro Takatsuka
Graph Neural Network Graph Learning Graph Convolutional Network Graph Convolution Graph Based Learning Graph Construction Linear Model Tree

February 7, 2023

January 24, 2023

Neighborhood Homophily-based Graph Convolutional Network
Shengbo Gong, Jiajun Zhou, Chenxuan Xie, Qi Xuan
Deep Convolutional Neural Network Graph Convolutional Network Real World Graph Neighborhood Level

January 11, 2023

Beyond Graph Convolutional Network: An Interpretable Regularizer-centered Optimization Framework
Shiping Wang, Zhihao Wu, Yuhong Chen, Yong Chen
Convolutional Neural Network Deep Convolutional Neural Network Graph Convolutional Network Graph Convolution Graph Regularization

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 31, 2022

Attentional Graph Convolutional Network for Structure-aware Audio-Visual Scene Classification
Liguang Zhou, Yuhongze Zhou, Xiaonan Qi, Junjie Hu, Tin Lun Lam, Yangsheng Xu
Graph Convolutional Network Audio Visual Scene

December 19, 2022

Training Lightweight Graph Convolutional Networks with Phase-field Models
Hichem Sahbi
Convolutional Neural Network Graph Convolutional Network lightWeight Network Phase Field

December 18, 2022

Graph Neural Network based Child Activity Recognition
Sanka Mohottala, Pradeepa Samarasinghe, Dharshana Kasthurirathna, Charith Abhayaratne
Graph Neural Network Activity Recognition Graph Convolutional Network Action Datasets