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

October 3, 2022

Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples
Wei Duan, Junyu Xuan, Maoying Qiao, Jie Lu
LeArning Abstract Representation Learning Node Representation Negative Sample Graph Convolutional Neural Network

September 26, 2022

A Simple Way to Learn Metrics Between Attributed Graphs
Yacouba Kaloga, Pierre Borgnat, Amaury Habrard
Metric Learning Metric Library Graph Convolutional Neural Network Graph Metric Attributed Graph Differentiable Metric

August 11, 2022

July 22, 2022

Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules
Jong Youl Choi, Pei Zhang, Kshitij Mehta, Andrew Blanchard, Massimiliano Lupo Pasini
Convolutional Neural Network Graph Drawing Gene Level GNN Molecular Graph Known Molecule Graph Convolutional Neural Network Large Scale Graph Datasets Scalable Training Large Scale GNN

July 13, 2022

Graph CNN for Moving Object Detection in Complex Environments from Unseen Videos
Jhony H. Giraldo, Sajid Javed, Naoufel Werghi, Thierry Bouwmans
Change Detection Complex Environment Graph Convolutional Neural Network Graph Convolutional Unseen Video Background Subtraction Moving Object Detection

July 7, 2022

Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling
Hongkang Li, Meng Wang, Sijia Liu, Pin-Yu Chen, Jinjun Xiong
Convolutional Neural Network Graph Structured Data Graph Convolutional Neural Network Semi Supervised Node Classification Generalization Guarantee Graph Topology Graph Sampling

July 5, 2022

What Do Graph Convolutional Neural Networks Learn?
Sannat Singh Bhasin, Vaibhav Holani, Divij Sanjanwala
Graph Convolutional Network Graph Structure Node Embeddings Heterophilic Graph Graph Convolutional Neural Network Semi Supervised Node Classification

June 30, 2022

Graph-Time Convolutional Neural Networks: Architecture and Theoretical Analysis
Mohammad Sabbaqi, Elvin Isufi
Temporal Dependency Temporal Graph Architecture Design Theoretical Analysis Graph Convolutional Neural Network SpatioTemporal Data Spatiotemporal Network

June 22, 2022

Understanding convolution on graphs via energies
Francesco Di Giovanni, James Rowbottom, Benjamin P. Chamberlain, Thomas Markovich, Michael M. Bronstein
Graph Neural Network Graph Drawing Direct Convolution Graph Convolution Energy Policy Research Graph Convolutional Neural Network

June 11, 2022

Parameter Convex Neural Networks
Jingcheng Zhou, Wei Wei, Xing Li, Bowen Pang, Zhiming Zheng
Deep Convolutional Neural Network Convex Function Graph Convolutional Neural Network Emergent Convexity Convex Neural Network Exponential Activation Function

June 10, 2022

Beyond the Gates of Euclidean Space: Temporal-Discrimination-Fusions and Attention-based Graph Neural Network for Human Activity Recognition
Nafees Ahmad, Savio Ho-Chit Chow, Ho-fung Leung
Graph Neural Network Activity Recognition Content Based Feature Graph Convolutional Neural Network Temporal Fusion Euclidean Space Graph Convolutional Attention Based Graph Neural Network

May 21, 2022

Travel Time, Distance and Costs Optimization for Paratransit Operations using Graph Convolutional Neural Network
Kelvin Kwakye, Younho Seong, Sun Yi
Graph Convolutional Neural Network Distance Matter Travel Time Paratransit Service

April 4, 2022

Learning to solve Minimum Cost Multicuts efficiently using Edge-Weighted Graph Convolutional Neural Networks
Steffen Jung, Margret Keuper
LeArning Abstract Graph Convolutional Neural Network

March 15, 2022

Graph Convolutional Neural Networks Sensitivity under Probabilistic Error Model
Xinjue Wang, Esa Ollila, Sergiy A. Vorobyov
Graph Neural Network Graph Convolutional Neural Network Graph Isomorphism Network Graph Shift Operator Error Model

March 3, 2022

Pay Attention to Relations: Multi-embeddings for Attributed Multiplex Networks
Joshua Melton, Michael Ridenhour, Siddharth Krishnan
Graph Convolutional Neural Network Inter Relation Pay Attention Multiplex Network Multiple Embeddings Homogeneous Diffusive Network

February 21, 2022

Data-Driven Traffic Assignment: A Novel Approach for Learning Traffic Flow Patterns Using a Graph Convolutional Neural Network
Rezaur Rahman, Samiul Hasan
Novel Approach Graph Convolutional Neural Network Traffic Pattern Transportation Network Traffic Assignment Driven Network

December 29, 2021

Deformable Graph Convolutional Networks
Jinyoung Park, Sungdong Yoo, Jihwan Park, Hyunwoo J. Kim
Graph Convolution Node Representation Graph Convolutional Neural Network Positional Embeddings

December 27, 2021

Block Modeling-Guided Graph Convolutional Neural Networks
Dongxiao He, Chundong Liang, Huixin Liu, Mingxiang Wen, Pengfei Jiao, Zhiyong Feng
Graph Representation Graph Convolutional Network Graph Convolutional Neural Network Multi Modal Outer Arithmetic Block

December 22, 2021

ML4CO: Is GCNN All You Need? Graph Convolutional Neural Networks Produce Strong Baselines For Combinatorial Optimization Problems, If Tuned and Trained Properly, on Appropriate Data
Amin Banitalebi-Dehkordi, Yong Zhang
Combinatorial Optimization Problem Strong Baseline Graph Convolutional Neural Network GNN Cl Combinatorial Optimization Solver

Graph Convolutional Neural Network

Papers

Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples

A Simple Way to Learn Metrics Between Attributed Graphs

Partition Pooling for Convolutional Graph Network Applications in Particle Physics

Multi-Agent Reinforcement Learning with Graph Convolutional Neural Networks for optimal Bidding Strategies of Generation Units in Electricity Markets

Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules

Graph CNN for Moving Object Detection in Complex Environments from Unseen Videos

Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling

What Do Graph Convolutional Neural Networks Learn?

Graph-Time Convolutional Neural Networks: Architecture and Theoretical Analysis

Understanding convolution on graphs via energies

Parameter Convex Neural Networks

Beyond the Gates of Euclidean Space: Temporal-Discrimination-Fusions and Attention-based Graph Neural Network for Human Activity Recognition

Travel Time, Distance and Costs Optimization for Paratransit Operations using Graph Convolutional Neural Network

Learning to solve Minimum Cost Multicuts efficiently using Edge-Weighted Graph Convolutional Neural Networks

Graph Convolutional Neural Networks Sensitivity under Probabilistic Error Model

Pay Attention to Relations: Multi-embeddings for Attributed Multiplex Networks

Data-Driven Traffic Assignment: A Novel Approach for Learning Traffic Flow Patterns Using a Graph Convolutional Neural Network

Deformable Graph Convolutional Networks

Block Modeling-Guided Graph Convolutional Neural Networks

ML4CO: Is GCNN All You Need? Graph Convolutional Neural Networks Produce Strong Baselines For Combinatorial Optimization Problems, If Tuned and Trained Properly, on Appropriate Data