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
Unveiling Molecular Moieties through Hierarchical Graph Explainability
Paolo Sortino, Salvatore Contino, Ugo Perricone, Roberto Pirrone
Enhancing Molecular Property Prediction with Auxiliary Learning and Task-Specific Adaptation
Vishal Dey, Xia Ning
Combined track finding with GNN & CKF
Lukas Heinrich, Benjamin Huth, Andreas Salzburger, Tilo Wettig
A Gated MLP Architecture for Learning Topological Dependencies in Spatio-Temporal Graphs
Yun Young Choi, Minho Lee, Sun Woo Park, Seunghwan Lee, Joohwan Ko
Towards Causal Classification: A Comprehensive Study on Graph Neural Networks
Simi Job, Xiaohui Tao, Taotao Cai, Lin Li, Haoran Xie, Jianming Yong
Neutrino Reconstruction in TRIDENT Based on Graph Neural Network
Cen Mo, Fuyudi Zhang, Liang Li
Adaptive Least Mean Squares Graph Neural Networks and Online Graph Signal Estimation
Yi Yan, Changran Peng, Ercan Engin Kuruoglu
SupplyGraph: A Benchmark Dataset for Supply Chain Planning using Graph Neural Networks
Azmine Toushik Wasi, MD Shafikul Islam, Adipto Raihan Akib
FedGT: Federated Node Classification with Scalable Graph Transformer
Zaixi Zhang, Qingyong Hu, Yang Yu, Weibo Gao, Qi Liu
Design Your Own Universe: A Physics-Informed Agnostic Method for Enhancing Graph Neural Networks
Dai Shi, Andi Han, Lequan Lin, Yi Guo, Zhiyong Wang, Junbin Gao
GOAt: Explaining Graph Neural Networks via Graph Output Attribution
Shengyao Lu, Keith G. Mills, Jiao He, Bang Liu, Di Niu
Alleviating Structural Distribution Shift in Graph Anomaly Detection
Yuan Gao, Xiang Wang, Xiangnan He, Zhenguang Liu, Huamin Feng, Yongdong Zhang
Choosing a Classical Planner with Graph Neural Networks
Jana Vatter, Ruben Mayer, Hans-Arno Jacobsen, Horst Samulowitz, Michael Katz
Edge Conditional Node Update Graph Neural Network for Multi-variate Time Series Anomaly Detection
Hayoung Jo, Seong-Whan Lee
Probabilistic Demand Forecasting with Graph Neural Networks
Nikita Kozodoi, Elizaveta Zinovyeva, Simon Valentin, João Pereira, Rodrigo Agundez
Truck Parking Usage Prediction with Decomposed Graph Neural Networks
Rei Tamaru, Yang Cheng, Steven Parker, Ernie Perry, Bin Ran, Soyoung Ahn
MAPPING: Debiasing Graph Neural Networks for Fair Node Classification with Limited Sensitive Information Leakage
Ying Song, Balaji Palanisamy
DeepRicci: Self-supervised Graph Structure-Feature Co-Refinement for Alleviating Over-squashing
Li Sun, Zhenhao Huang, Hua Wu, Junda Ye, Hao Peng, Zhengtao Yu, Philip S. Yu