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
Spatio-Spectral Graph Neural Networks
Simon Geisler, Arthur Kosmala, Daniel Herbst, Stephan Günnemann
CiliaGraph: Enabling Expression-enhanced Hyper-Dimensional Computation in Ultra-Lightweight and One-Shot Graph Classification on Edge
Yuxi Han, Jihe Wang, Danghui Wang
Spatiotemporal Forecasting Meets Efficiency: Causal Graph Process Neural Networks
Aref Einizade, Fragkiskos D. Malliaros, Jhony H. Giraldo
DeepHGNN: Study of Graph Neural Network based Forecasting Methods for Hierarchically Related Multivariate Time Series
Abishek Sriramulu, Nicolas Fourrier, Christoph Bergmeir
Improving global awareness of linkset predictions using Cross-Attentive Modulation tokens
Félix Marcoccia, Cédric Adjih, Paul Mühlethaler
Unleashing the Potential of Text-attributed Graphs: Automatic Relation Decomposition via Large Language Models
Hyunjin Seo, Taewon Kim, June Yong Yang, Eunho Yang
Injecting Hierarchical Biological Priors into Graph Neural Networks for Flow Cytometry Prediction
Fatemeh Nassajian Mojarrad, Lorenzo Bini, Thomas Matthes, Stéphane Marchand-Maillet
Graph Coarsening with Message-Passing Guarantees
Antonin Joly, Nicolas Keriven
ForecastGrapher: Redefining Multivariate Time Series Forecasting with Graph Neural Networks
Wanlin Cai, Kun Wang, Hao Wu, Xiaoxu Chen, Yuankai Wu
Revisiting the Message Passing in Heterophilous Graph Neural Networks
Zhuonan Zheng, Yuanchen Bei, Sheng Zhou, Yao Ma, Ming Gu, HongJia XU, Chengyu Lai, Jiawei Chen, Jiajun Bu
Spectral Greedy Coresets for Graph Neural Networks
Mucong Ding, Yinhan He, Jundong Li, Furong Huang
Survey of Graph Neural Network for Internet of Things and NextG Networks
Sabarish Krishna Moorthy, Jithin Jagannath
Graph Neural Networks on Quantum Computers
Yidong Liao, Xiao-Ming Zhang, Chris Ferrie
FUGNN: Harmonizing Fairness and Utility in Graph Neural Networks
Renqiang Luo, Huafei Huang, Shuo Yu, Zhuoyang Han, Estrid He, Xiuzhen Zhang, Feng Xia
Message-Passing Monte Carlo: Generating low-discrepancy point sets via Graph Neural Networks
T. Konstantin Rusch, Nathan Kirk, Michael M. Bronstein, Christiane Lemieux, Daniela Rus
Analysis of Atom-level pretraining with Quantum Mechanics (QM) data for Graph Neural Networks Molecular property models
Jose Arjona-Medina, Ramil Nugmanov