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 - Page 15
Towards Precise Prediction Uncertainty in GNNs: Refining GNNs with Topology-grouping Strategy
Hyunjin Seo, Kyusung Seo, Joonhyung Park, Eunho YangSpatio-Temporal Forecasting of PM2.5 via Spatial-Diffusion guided Encoder-Decoder Architecture
Malay Pandey, Vaishali Jain, Nimit Godhani, Sachchida Nand Tripathi, Piyush RaiGraph Coarsening via Supervised Granular-Ball for Scalable Graph Neural Network Training
Shuyin Xia, Xinjun Ma, Zhiyuan Liu, Cheng Liu, Sen Zhao, Guoyin Wang
Enhancing Persona Classification in Dialogue Systems: A Graph Neural Network Approach
Konstantin ZaitsevTowards Effective Graph Rationalization via Boosting Environment Diversity
Yujie Wang, Kui Yu, Yuhong Zhang, Fuyuan Cao, Jiye LiangGraph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks
Xunkai Li, Zhengyu Wu, Jiayi Wu, Hanwen Cui, Jishuo Jia, Rong-Hua Li, Guoren Wang
Cost-Effective Label-free Node Classification with LLMs
Taiyan Zhang, Renchi Yang, Mingyu Yan, Xiaochun Ye, Dongrui Fan, Yurui LaiBetaExplainer: A Probabilistic Method to Explain Graph Neural Networks
Whitney Sloneker, Shalin Patel, Michael Wang, Lorin Crawford, Ritambhara SinghAccelerating Sparse Graph Neural Networks with Tensor Core Optimization
Ka Wai Wu
A Comparative Study on Dynamic Graph Embedding based on Mamba and Transformers
Ashish Parmanand Pandey, Alan John Varghese, Sarang Patil, Mengjia XuConcept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks
Elad Shohama, Hadar Cohena, Khalil Wattada, Havana Rikab, Dan VilenchikGNNs-to-MLPs by Teacher Injection and Dirichlet Energy Distillation
Ziang Zhou, Zhihao Ding, Jieming Shi, Qing Li, Shiqi Shen
Shape error prediction in 5-axis machining using graph neural networks
Julia Huuk, Abheek Dhingra, Eirini Ntoutsi, Bernd DenkenaA Hybrid Real-Time Framework for Efficient Fussell-Vesely Importance Evaluation Using Virtual Fault Trees and Graph Neural Networks
Xingyu Xiao, Peng ChenGraSP: Simple yet Effective Graph Similarity Predictions
Haoran Zheng, Jieming Shi, Renchi YangTowards Fair Graph Neural Networks via Graph Counterfactual without Sensitive Attributes
Xuemin Wang, Tianlong Gu, Xuguang Bao, Liang Chang