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
You Can't Ignore Either: Unifying Structure and Feature Denoising for Robust Graph Learning
Tianmeng Yang, Jiahao Meng, Min Zhou, Yaming Yang, Yujing Wang, Xiangtai Li, Yunhai Tong
Contrastive Graph Representation Learning with Adversarial Cross-view Reconstruction and Information Bottleneck
Yuntao Shou, Haozhi Lan, Xiangyong Cao
Multi-Modal Parameter-Efficient Fine-tuning via Graph Neural Network
Bin Cheng, Jiaxuan Lu
GNUMAP: A Parameter-Free Approach to Unsupervised Dimensionality Reduction via Graph Neural Networks
Jihee You, So Won Jeong, Claire Donnat
Joint Diffusion Processes as an Inductive Bias in Sheaf Neural Networks
Ferran Hernandez Caralt, Guillermo Bernárdez Gil, Iulia Duta, Pietro Liò, Eduard Alarcón Cot
Optimizing Long-tailed Link Prediction in Graph Neural Networks through Structure Representation Enhancement
Yakun Wang, Daixin Wang, Hongrui Liu, Binbin Hu, Yingcui Yan, Qiyang Zhang, Zhiqiang Zhang
Robust Learning in Bayesian Parallel Branching Graph Neural Networks: The Narrow Width Limit
Zechen Zhang, Haim Sompolinsky
Graph Neural Networks for Virtual Sensing in Complex Systems: Addressing Heterogeneous Temporal Dynamics
Mengjie Zhao, Cees Taal, Stephan Baggerohr, Olga Fink
DTFormer: A Transformer-Based Method for Discrete-Time Dynamic Graph Representation Learning
Xi Chen, Yun Xiong, Siwei Zhang, Jiawei Zhang, Yao Zhang, Shiyang Zhou, Xixi Wu, Mingyang Zhang, Tengfei Liu, Weiqiang Wang