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
Highly Accurate Disease Diagnosis and Highly Reproducible Biomarker Identification with PathFormer
Zehao Dong, Qihang Zhao, Philip R. O. Payne, Michael A Province, Carlos Cruchaga, Muhan Zhang, Tianyu Zhao, Yixin Chen, Fuhai Li
Explainable Global Wildfire Prediction Models using Graph Neural Networks
Dayou Chen, Sibo Cheng, Jinwei Hu, Matthew Kasoar, Rossella Arcucci
Rethinking the Capacity of Graph Neural Networks for Branching Strategy
Ziang Chen, Jialin Liu, Xiaohan Chen, Xinshang Wang, Wotao Yin
Deceptive Path Planning via Reinforcement Learning with Graph Neural Networks
Michael Y. Fatemi, Wesley A. Suttle, Brian M. Sadler
Flexible infinite-width graph convolutional networks and the importance of representation learning
Ben Anson, Edward Milsom, Laurence Aitchison
CoRe-GD: A Hierarchical Framework for Scalable Graph Visualization with GNNs
Florian Grötschla, Joël Mathys, Robert Veres, Roger Wattenhofer
N-1 Reduced Optimal Power Flow Using Augmented Hierarchical Graph Neural Network
Thuan Pham, Xingpeng Li
Game-theoretic Counterfactual Explanation for Graph Neural Networks
Chirag Chhablani, Sarthak Jain, Akshay Channesh, Ian A. Kash, Sourav Medya
Classifying Nodes in Graphs without GNNs
Daniel Winter, Niv Cohen, Yedid Hoshen
Large Language Model Meets Graph Neural Network in Knowledge Distillation
Shengxiang Hu, Guobing Zou, Song Yang, Yanglan Gan, Bofeng Zhang, Yixin Chen
Graph Neural Networks for Physical-Layer Security in Multi-User Flexible-Duplex Networks
Tharaka Perera, Saman Atapattu, Yuting Fang, Jamie Evans
Graph Neural Networks as Fast and High-fidelity Emulators for Finite-Element Ice Sheet Modeling
Maryam Rahnemoonfar, Younghyun Koo
Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching
Yuchen Zhang, Tianle Zhang, Kai Wang, Ziyao Guo, Yuxuan Liang, Xavier Bresson, Wei Jin, Yang You
Incorporating Retrieval-based Causal Learning with Information Bottlenecks for Interpretable Graph Neural Networks
Jiahua Rao, Jiancong Xie, Hanjing Lin, Shuangjia Zheng, Zhen Wang, Yuedong Yang