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
On the Topology Awareness and Generalization Performance of Graph Neural Networks
Junwei Su, Chuan Wu
A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges
Wei Ju, Siyu Yi, Yifan Wang, Zhiping Xiao, Zhengyang Mao, Hourun Li, Yiyang Gu, Yifang Qin, Nan Yin, Senzhang Wang, Xinwang Liu, Xiao Luo, Philip S. Yu, Ming Zhang
Predicting the Temperature Dependence of Surfactant CMCs Using Graph Neural Networks
Christoforos Brozos, Jan G. Rittig, Sandip Bhattacharya, Elie Akanny, Christina Kohlmann, Alexander Mitsos
Simplified PCNet with Robustness
Bingheng Li, Xuanting Xie, Haoxiang Lei, Ruiyi Fang, Zhao Kang
Provable Filter for Real-world Graph Clustering
Xuanting Xie, Erlin Pan, Zhao Kang, Wenyu Chen, Bingheng Li
K-Link: Knowledge-Link Graph from LLMs for Enhanced Representation Learning in Multivariate Time-Series Data
Yucheng Wang, Ruibing Jin, Min Wu, Xiaoli Li, Lihua Xie, Zhenghua Chen
Learning Invariant Representations of Graph Neural Networks via Cluster Generalization
Donglin Xia, Xiao Wang, Nian Liu, Chuan Shi
A Teacher-Free Graph Knowledge Distillation Framework with Dual Self-Distillation
Lirong Wu, Haitao Lin, Zhangyang Gao, Guojiang Zhao, Stan Z. Li
Graph Theory and GNNs to Unravel the Topographical Organization of Brain Lesions in Variants of Alzheimer's Disease Progression
Leopold Hebert-Stevens, Gabriel Jimenez, Benoit Delatour, Lev Stimmer, Daniel Racoceanu
A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications
Jiaqi Han, Jiacheng Cen, Liming Wu, Zongzhao Li, Xiangzhe Kong, Rui Jiao, Ziyang Yu, Tingyang Xu, Fandi Wu, Zihe Wang, Hongteng Xu, Zhewei Wei, Yang Liu, Yu Rong, Wenbing Huang
Nonlinear Sheaf Diffusion in Graph Neural Networks
Olga Zaghen
GNSS Positioning using Cost Function Regulated Multilateration and Graph Neural Networks
Amir Jalalirad, Davide Belli, Bence Major, Songwon Jee, Himanshu Shah, Will Morrison
Challenges in Pre-Training Graph Neural Networks for Context-Based Fake News Detection: An Evaluation of Current Strategies and Resource Limitations
Gregor Donabauer, Udo Kruschwitz
Using Graph Neural Networks to Predict Local Culture
Thiago H Silva, Daniel Silver
Predicting Instability in Complex Oscillator Networks: Limitations and Potentials of Network Measures and Machine Learning
Christian Nauck, Michael Lindner, Nora Molkenthin, Jürgen Kurths, Eckehard Schöll, Jörg Raisch, Frank Hellmann