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
Path Signatures and Graph Neural Networks for Slow Earthquake Analysis: Better Together?
Hans Riess, Manolis Veveakis, Michael M. Zavlanos
Single-GPU GNN Systems: Traps and Pitfalls
Yidong Gong, Arnab Tarafder, Saima Afrin, Pradeep Kumar
Physics-Encoded Graph Neural Networks for Deformation Prediction under Contact
Mahdi Saleh, Michael Sommersperger, Nassir Navab, Federico Tombari
Statistical Guarantees for Link Prediction using Graph Neural Networks
Alan Chung, Amin Saberi, Morgane Austern
Graph Neural Network and NER-Based Text Summarization
Imaad Zaffar Khan, Amaan Aijaz Sheikh, Utkarsh Sinha
SemPool: Simple, robust, and interpretable KG pooling for enhancing language models
Costas Mavromatis, Petros Karypis, George Karypis
Generating In-Distribution Proxy Graphs for Explaining Graph Neural Networks
Zhuomin Chen, Jiaxing Zhang, Jingchao Ni, Xiaoting Li, Yuchen Bian, Md Mezbahul Islam, Ananda Mohan Mondal, Hua Wei, Dongsheng Luo
Topology-Informed Graph Transformer
Yun Young Choi, Sun Woo Park, Minho Lee, Youngho Woo
Two Heads Are Better Than One: Boosting Graph Sparse Training via Semantic and Topological Awareness
Guibin Zhang, Yanwei Yue, Kun Wang, Junfeng Fang, Yongduo Sui, Kai Wang, Yuxuan Liang, Dawei Cheng, Shirui Pan, Tianlong Chen
Unveiling Delay Effects in Traffic Forecasting: A Perspective from Spatial-Temporal Delay Differential Equations
Qingqing Long, Zheng Fang, Chen Fang, Chong Chen, Pengfei Wang, Yuanchun Zhou
Graph Neural Networks in EEG-based Emotion Recognition: A Survey
Chenyu Liu, Xinliang Zhou, Yihao Wu, Ruizhi Yang, Zhongruo Wang, Liming Zhai, Ziyu Jia, Yang Liu
DoseGNN: Improving the Performance of Deep Learning Models in Adaptive Dose-Volume Histogram Prediction through Graph Neural Networks
Zehao Dong, Yixin Chen, Tianyu Zhao
Benchmarking Sensitivity of Continual Graph Learning for Skeleton-Based Action Recognition
Wei Wei, Tom De Schepper, Kevin Mets
PF-GNN: Differentiable particle filtering based approximation of universal graph representations
Mohammed Haroon Dupty, Yanfei Dong, Wee Sun Lee
IGCN: Integrative Graph Convolution Networks for patient level insights and biomarker discovery in multi-omics integration
Cagri Ozdemir, Mohammad Al Olaimat, Yashu Vashishath, Serdar Bozdag, Alzheimer's Disease Neuroimaging Initiative