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

December 17, 2021

Community-based Layerwise Distributed Training of Graph Convolutional Networks
Hongyi Li, Junxiang Wang, Yongchao Wang, Yue Cheng, Liang Zhao
Training Data Graph Convolutional Network Layer Wise Alternating Direction Method of Multiplier

December 13, 2021

GCNDepth: Self-supervised Monocular Depth Estimation based on Graph Convolutional Network
Armin Masoumian, Hatem A. Rashwan, Saddam Abdulwahab, Julian Cristiano, Domenec Puig
Convolutional Neural Network Depth Estimation Monocular Depth Estimation Graph Convolutional Network Depth Map Self Supervised Depth

December 6, 2021

An Effective GCN-based Hierarchical Multi-label classification for Protein Function Prediction
Kyudam Choi, Yurim Lee, Cheongwon Kim, Minsung Yoon
Graph Convolutional Network Node Representation Protein Sequence Protein Function Prediction Hierarchical Multi Label Classification Gene Ontology

December 5, 2021

SSAGCN: Social Soft Attention Graph Convolution Network for Pedestrian Trajectory Prediction
Pei Lv, Wentong Wang, Yunxin Wang, Yuzhen Zhang, Mingliang Xu, Changsheng Xu
Trajectory Prediction Graph Convolutional Network Pedestrian Trajectory Prediction

November 29, 2021

PGGANet: Pose Guided Graph Attention Network for Person Re-identification
Zhijun He, Hongbo Zhao, Wenquan Feng
Human Pose Person Re Identification Graph Convolutional Network Graph Attention Network Pose Information

November 27, 2021

Benchmarking Accuracy and Generalizability of Four Graph Neural Networks Using Large In Vitro ADME Datasets from Different Chemical Spaces
Fabio Broccatelli, Richard Trager, Michael Reutlinger, George Karypis, Mufei Li
Graph Neural Network Graph Convolutional Network Stronger Generalizability Benchmark Study Chemical Space Graph Neural Network Model Bioactivity Data

Graph Convolutional Network

Papers

Community-based Layerwise Distributed Training of Graph Convolutional Networks

GCNDepth: Self-supervised Monocular Depth Estimation based on Graph Convolutional Network

An Effective GCN-based Hierarchical Multi-label classification for Protein Function Prediction

SSAGCN: Social Soft Attention Graph Convolution Network for Pedestrian Trajectory Prediction

PGGANet: Pose Guided Graph Attention Network for Person Re-identification

Benchmarking Accuracy and Generalizability of Four Graph Neural Networks Using Large In Vitro ADME Datasets from Different Chemical Spaces