Graph Neural Network

Graph Neural Networks (GNNs) are a class of machine learning models designed to analyze and learn from data represented as graphs, focusing on capturing relationships between nodes and their impact on downstream tasks like node classification and link prediction. Current research emphasizes improving GNN performance by addressing limitations such as oversmoothing and oversquashing through architectural innovations (e.g., incorporating residual connections, Cayley graph propagation) and novel training techniques (e.g., contrastive learning, Laplacian regularization). GNNs are proving valuable across diverse fields, including social network analysis, drug discovery, and financial modeling, offering powerful tools for analyzing complex relational data where traditional methods fall short.

Papers

October 15, 2024

Regional Ocean Forecasting with Hierarchical Graph Neural Networks
Daniel Holmberg, Emanuela Clementi, Teemu Roos
Graph Neural Network Upper Ocean Grid Graph
ECGN: A Cluster-Aware Approach to Graph Neural Networks for Imbalanced Classification
Bishal Thapaliya, Anh Nguyen, Yao Lu, Tian Xie, Igor Grudetskyi, Fudong Lin, Antonios Valkanas, Jingyu Liu, Deepayan Chakraborty, Bilel Fehri
Graph Neural Network Classification Code Node Classification Node Embeddings Graph Clustering Well Clustered Graph
Towards Fair Graph Representation Learning in Social Networks
Guixian Zhang, Guan Yuan, Debo Cheng, Lin Liu, Jiuyong Li, Shichao Zhang
Graph Neural Network Social Network Group Fairness Network Learning Fair Graph
KA-GNN: Kolmogorov-Arnold Graph Neural Networks for Molecular Property Prediction
Longlong Li, Yipeng Zhang, Guanghui Wang, Kelin Xia
Graph Neural Network Human Prediction Kolmogorov Arnold Network Molecular Property Prediction Molecular Property GNN Cl
Rethinking Graph Transformer Architecture Design for Node Classification
Jiajun Zhou, Xuanze Chen, Chenxuan Xie, Yu Shanqing, Qi Xuan, Xiaoniu Yang
Graph Neural Network Graph Transformer Node Classification GNN Cl

October 14, 2024

GraFPrint: A GNN-Based Approach for Audio Identification
Aditya Bhattacharjee, Shubhr Singh, Emmanouil Benetos
Graph Neural Network Time Frequency Self Supervised Contrastive Learning GNN Based Audio Understanding Audio Fingerprint
Arrhythmia Classification Using Graph Neural Networks Based on Correlation Matrix
Seungwoo Han
Graph Neural Network Adjacency Matrix ECG Analysis Correlation Matrix
NT-LLM: A Novel Node Tokenizer for Integrating Graph Structure into Large Language Models
Yanbiao Ji, Chang Liu, Xin Chen, Yue Ding, Dan Luo, Mei Li, Wenqing Lin, Hongtao Lu
Graph Neural Network Graph Learning Graph Level Task Text to Graph LLM RBMT Graph Integration
Graph Classification Gaussian Processes via Hodgelet Spectral Features
Mathieu Alain, So Takao, Xiaowen Dong, Bastian Rieck, Emmanuel Noutahi
Graph Neural Network Gaussian Process Spectral Feature Graph Data Graph Feature Edge Attribute

October 12, 2024

October 11, 2024

Predicting Drug Effects from High-Dimensional, Asymmetric Drug Datasets by Using Graph Neural Networks: A Comprehensive Analysis of Multitarget Drug Effect Prediction
Avishek Bose, Guojing Cong
Graph Neural Network Molecular Graph Drug Feature Drug Target Multilabel Classification Side Effect Effect Prediction
When Graph meets Multimodal: Benchmarking on Multimodal Attributed Graphs Learning
Hao Yan, Chaozhuo Li, Zhigang Yu, Jun Yin, Ruochen Liu, Peiyan Zhang, Weihao Han, Mingzheng Li, Zhengxin Zeng, Hao Sun, Weiwei Deng, Feng Sun, Qi Zhang, Senzhang Wang
Graph Neural Network Graph Drawing Multimodal Phenomenon Graph Learning Multi Modal Feature Graph Topology
Enhancing GNNs with Architecture-Agnostic Graph Transformations: A Systematic Analysis
Zhifei Li, Gerrit Großmann, Verena Wolf
Graph Neural Network Gene Level GNN Discriminative Graph Graph Transformation
IGNN-Solver: A Graph Neural Solver for Implicit Graph Neural Networks
Junchao Lin, Zenan Ling, Zhanbo Feng, Feng Zhou, Jingwen Xu, Robert C Qiu
Graph Neural Network Large Scale Graph Neural Solver Fixed Point Fixed Point Iteration Implicit Graph Neural Network

Graph Neural Network

Papers

Regional Ocean Forecasting with Hierarchical Graph Neural Networks

ECGN: A Cluster-Aware Approach to Graph Neural Networks for Imbalanced Classification

Towards Fair Graph Representation Learning in Social Networks

KA-GNN: Kolmogorov-Arnold Graph Neural Networks for Molecular Property Prediction

Rethinking Graph Transformer Architecture Design for Node Classification

GraFPrint: A GNN-Based Approach for Audio Identification

Arrhythmia Classification Using Graph Neural Networks Based on Correlation Matrix

NT-LLM: A Novel Node Tokenizer for Integrating Graph Structure into Large Language Models

Graph Classification Gaussian Processes via Hodgelet Spectral Features

Towards characterizing the value of edge embeddings in Graph Neural Networks

Towards Stable, Globally Expressive Graph Representations with Laplacian Eigenvectors

Control the GNN: Utilizing Neural Controller with Lyapunov Stability for Test-Time Feature Reconstruction

Bayesian Sheaf Neural Networks

GETS: Ensemble Temperature Scaling for Calibration in Graph Neural Networks

Eco-Aware Graph Neural Networks for Sustainable Recommendations

BANGS: Game-Theoretic Node Selection for Graph Self-Training

Predicting Drug Effects from High-Dimensional, Asymmetric Drug Datasets by Using Graph Neural Networks: A Comprehensive Analysis of Multitarget Drug Effect Prediction

When Graph meets Multimodal: Benchmarking on Multimodal Attributed Graphs Learning

Enhancing GNNs with Architecture-Agnostic Graph Transformations: A Systematic Analysis

IGNN-Solver: A Graph Neural Solver for Implicit Graph Neural Networks