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
Graph Neural Networks for Parameterized Quantum Circuits Expressibility Estimation
Shamminuj Aktar, Andreas Bärtschi, Diane Oyen, Stephan Eidenbenz, Abdel-Hameed A. Badawy
All Nodes are created Not Equal: Node-Specific Layer Aggregation and Filtration for GNN
Shilong Wang, Hao Wu, Yifan Duan, Guibin Zhang, Guohao Li, Yuxuan Liang, Shirui Pan, Kun Wang, Yang Wang
UnSegGNet: Unsupervised Image Segmentation using Graph Neural Networks
Kovvuri Sai Gopal Reddy, Bodduluri Saran, A. Mudit Adityaja, Saurabh J. Shigwan, Nitin Kumar
Deploying Graph Neural Networks in Wireless Networks: A Link Stability Viewpoint
Jun Li, Weiwei Zhang, Kang Wei, Guangji Chen, Long Shi, Wen Chen
DiskGNN: Bridging I/O Efficiency and Model Accuracy for Out-of-Core GNN Training
Renjie Liu, Yichuan Wang, Xiao Yan, Zhenkun Cai, Minjie Wang, Haitian Jiang, Bo Tang, Jinyang Li
Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural Networks
Rongrong Ma, Guansong Pang, Ling Chen
Conditional Local Feature Encoding for Graph Neural Networks
Yongze Wang, Haimin Zhang, Qiang Wu, Min Xu
EiG-Search: Generating Edge-Induced Subgraphs for GNN Explanation in Linear Time
Shengyao Lu, Bang Liu, Keith G. Mills, Jiao He, Di Niu
ATNPA: A Unified View of Oversmoothing Alleviation in Graph Neural Networks
Yufei Jin, Xingquan Zhu
The Importance of Model Inspection for Better Understanding Performance Characteristics of Graph Neural Networks
Nairouz Shehata, Carolina Piçarra, Anees Kazi, Ben Glocker
IntraMix: Intra-Class Mixup Generation for Accurate Labels and Neighbors
Shenghe Zheng, Hongzhi Wang, Xianglong Liu