Graph Convolution

Graph convolution is a technique used in graph neural networks (GNNs) to process data represented as graphs, aiming to learn informative representations of nodes and edges by aggregating information from their neighbors. Current research focuses on improving efficiency and scalability, particularly for large graphs, exploring novel architectures like windowed GNNs and post-training methods to reduce computational complexity, and addressing issues like over-smoothing through techniques such as corrected convolutions and adaptive kernel approaches. These advancements are significant for various applications, including recommendation systems, image processing, medical image analysis, and human activity recognition, where graph-structured data is prevalent.

Papers

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

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

SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks
Thomas Monninger, Julian Schmidt, Jan Rupprecht, David Raba, Julian Jordan, Daniel Frank, Steffen Staab, Klaus Dietmayer
Node Classification Heterogeneous Graph Graph Convolution Heterogeneous Graph Neural Network Scene Structure Traffic Scene Node Feature

January 6, 2023

Graph Convolution Based Cross-Network Multi-Scale Feature Fusion for Deep Vessel Segmentation
Gangming Zhao, Kongming Liang, Chengwei Pan, Fandong Zhang, Xianpeng Wu, Xinyang Hu, Yizhou Yu
Graph Convolution Vessel Segmentation Multi Scale Feature Fusion 3D Vessel

December 21, 2022

A Non-Asymptotic Analysis of Oversmoothing in Graph Neural Networks
Xinyi Wu, Zhengdao Chen, William Wang, Ali Jadbabaie
Graph Neural Network Graph Convolution Random Graph Dense Graph Non Asymptotic Analysis

December 9, 2022

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

November 17, 2022

Spatial Graph Convolution Neural Networks for Water Distribution Systems
Inaam Ashraf, Luca Hermes, André Artelt, Barbara Hammer
Graph Drawing Graph Convolution Message Passing Multi Hop Sparse Signal Water Distribution Network

November 13, 2022

GC-GRU-N for Traffic Prediction using Loop Detector Data
Maged Shoman, Armstrong Aboah, Abdulateef Daud, Yaw Adu-Gyamfi
Long Short Term Memory Temporal Dependency Graph Convolution Traffic Prediction Loop Detection Stochastic Traffic

October 27, 2022

Training Graph Neural Networks on Growing Stochastic Graphs
Juan Cervino, Luana Ruiz, Alejandro Ribeiro
Graph Neural Network Graph Convolution Large Graph Stochastic Network Optimal Neural Network

October 18, 2022

October 14, 2022

Characterizing the Influence of Graph Elements
Zizhang Chen, Peizhao Li, Hongfu Liu, Pengyu Hong
Graph Convolution External Influence Influence Function Graph Convolution Network Graph Element

October 12, 2022

TwiRGCN: Temporally Weighted Graph Convolution for Question Answering over Temporal Knowledge Graphs
Aditya Sharma, Apoorv Saxena, Chitrank Gupta, Seyed Mehran Kazemi, Partha Talukdar, Soumen Chakrabarti
Knowledge Graph Yes No Question Graph Convolution Temporal Knowledge Graph Multi Hop Question Answering Temporal Reasoning Temporal Question

October 8, 2022

Explainable fMRI-based Brain Decoding via Spatial Temporal-pyramid Graph Convolutional Network
Ziyuan Ye, Youzhi Qu, Zhichao Liang, Mo Wang, Quanying Liu
Functional Magnetic Resonance Imaging Graph Convolution Spatial Domain Brain Decoding Human Connectome Project

October 6, 2022

Edge-Varying Fourier Graph Networks for Multivariate Time Series Forecasting
Kun Yi, Qi Zhang, Liang Hu, Hui He, Ning An, LongBing Cao, ZhenDong Niu
Multivariate Time Series Graph Convolution Multivariate Time Series Forecasting Graph Shift Operator Multidimensional Graph Fourier Transformation

October 1, 2022

Convolutional Neural Networks on Manifolds: From Graphs and Back
Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro
Convolutional Neural Network Graph Drawing Graph Convolution Class Manifold Statistical Manifold

September 30, 2022

GPNet: Simplifying Graph Neural Networks via Multi-channel Geometric Polynomials
Xun Liu, Alex Hay-Man Ng, Fangyuan Lei, Yikuan Zhang, Zhengmin Li
Deep Learning Graph Neural Network Graph Convolution Multi Channel

Graph Convolution

Papers

Attention-based Graph Convolution Fusing Latent Structures and Multiple Features for Graph Neural Networks

Graph Construction using Principal Axis Trees for Simple Graph Convolution

Beyond Graph Convolutional Network: An Interpretable Regularizer-centered Optimization Framework

SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks

Graph Convolution Based Cross-Network Multi-Scale Feature Fusion for Deep Vessel Segmentation

A Non-Asymptotic Analysis of Oversmoothing in Graph Neural Networks

Multi-view Graph Convolutional Networks with Differentiable Node Selection

Category-Level 6D Object Pose Estimation with Flexible Vector-Based Rotation Representation

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

Spatial Graph Convolution Neural Networks for Water Distribution Systems

GC-GRU-N for Traffic Prediction using Loop Detector Data

Training Graph Neural Networks on Growing Stochastic Graphs

On Classification Thresholds for Graph Attention with Edge Features

Analysis of Convolutions, Non-linearity and Depth in Graph Neural Networks using Neural Tangent Kernel

Characterizing the Influence of Graph Elements

TwiRGCN: Temporally Weighted Graph Convolution for Question Answering over Temporal Knowledge Graphs

Explainable fMRI-based Brain Decoding via Spatial Temporal-pyramid Graph Convolutional Network

Edge-Varying Fourier Graph Networks for Multivariate Time Series Forecasting

Convolutional Neural Networks on Manifolds: From Graphs and Back

GPNet: Simplifying Graph Neural Networks via Multi-channel Geometric Polynomials