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

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

December 19, 2023

Short-Term Multi-Horizon Line Loss Rate Forecasting of a Distribution Network Using Attention-GCN-LSTM
Jie Liu, Yijia Cao, Yong Li, Yixiu Guo, Wei Deng
Graph Convolutional Network Distribution System Multi Horizon Line Loss

December 15, 2023

MONET: Modality-Embracing Graph Convolutional Network and Target-Aware Attention for Multimedia Recommendation
Yungi Kim, Taeri Kim, Won-Yong Shin, Sang-Wook Kim
Human Attention Graph Convolutional Network GNN Based Multimodal Feature Higher Order Network Item Embeddings Multimedia Recommendation

December 14, 2023

A Sparse Cross Attention-based Graph Convolution Network with Auxiliary Information Awareness for Traffic Flow Prediction
Lingqiang Chen, Qinglin Zhao, Guanghui Li, Mengchu Zhou, Chenglong Dai, Yiming Feng
Many Sparse Graph Convolutional Network Graph Convolution Traffic Forecasting Traffic Flow Prediction Graph Convolutional Auxiliary Information GCN Based

November 27, 2023

ALNSynergy: a graph convolutional network with multi-representation alignment for drug synergy prediction
Xinxing Yang, Jiachen Li, Xiao Kang, Guojin Pei, Keyu Liu, Genke Yang, Jian Chu
Graph Convolutional Network Drug Representation Drug Synergy Drug Synergy Prediction Cross Representation

October 24, 2023

G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation
Md Mostafijur Rahman, Radu Marculescu
Graph Neural Network High Efficiency Graph Convolutional Network Medical Image Segmentation Task TF Cascade

October 22, 2023

Graph Convolutional Network with Connectivity Uncertainty for EEG-based Emotion Recognition
Hongxiang Gao, Xiangyao Wang, Zhenghua Chen, Min Wu, Zhipeng Cai, Lulu Zhao, Jianqing Li, Chengyu Liu
Emotion Recognition Graph Convolutional Network Sparse Connectivity Multi Channel EEG Automatic Emotion Recognition

October 18, 2023

GRI: Graph-based Relative Isomorphism of Word Embedding Spaces
Muhammad Asif Ali, Yan Hu, Jianbin Qin, Di Wang
Machine Translation Graph Convolutional Network Real Text Word Mixed Space Graph Isomorphism Bilingual Dictionary

October 12, 2023

2SFGL: A Simple And Robust Protocol For Graph-Based Fraud Detection
Zhirui Pan, Guangzhong Wang, Zhaoning Li, Lifeng Chen, Yang Bian, Zhongyuan Lai
Graph Convolutional Network Financial Fraud Detection Robust Communication Graph Based Fraud Detection

September 30, 2023

ResolvNet: A Graph Convolutional Network with multi-scale Consistency
Christian Koke, Abhishek Saroha, Yuesong Shen, Marvin Eisenberger, Daniel Cremers
Graph Convolutional Network Graph Propagation ResNet Architecture Graph Level Flexible Graph Parameter Oriented Scaling Consistency

September 26, 2023

A Comparative Study of Population-Graph Construction Methods and Graph Neural Networks for Brain Age Regression
Kyriaki-Margarita Bintsi, Tamara T. Mueller, Sophie Starck, Vasileios Baltatzis, Alexander Hammers, Daniel Rueckert
Graph Neural Network Comparative Study Graph Convolutional Network Brain Age Population Graph

Graph Convolutional Network

Papers

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

Short-Term Multi-Horizon Line Loss Rate Forecasting of a Distribution Network Using Attention-GCN-LSTM

MONET: Modality-Embracing Graph Convolutional Network and Target-Aware Attention for Multimedia Recommendation

A Sparse Cross Attention-based Graph Convolution Network with Auxiliary Information Awareness for Traffic Flow Prediction

ALNSynergy: a graph convolutional network with multi-representation alignment for drug synergy prediction

G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation

Graph Convolutional Network with Connectivity Uncertainty for EEG-based Emotion Recognition

GRI: Graph-based Relative Isomorphism of Word Embedding Spaces

2SFGL: A Simple And Robust Protocol For Graph-Based Fraud Detection

ResolvNet: A Graph Convolutional Network with multi-scale Consistency

A Comparative Study of Population-Graph Construction Methods and Graph Neural Networks for Brain Age Regression