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
Answer Set Networks: Casting Answer Set Programming into Deep Learning
Arseny Skryagin, Daniel Ochs, Phillip Deibert, Simon Kohaut, Devendra Singh Dhami, Kristian Kersting
Boosting Graph Neural Network Training by Focusing on Non-Robust Samples from the Training Set
Yongyu Wang
Tokenphormer: Structure-aware Multi-token Graph Transformer for Node Classification
Zijie Zhou, Zhaoqi Lu, Xuekai Wei, Rongqin Chen, Shenghui Zhang, Pak Lon Ip, Leong Hou U
IOHunter: Graph Foundation Model to Uncover Online Information Operations
Marco Minici, Luca Luceri, Francesco Fabbri, Emilio Ferrara
Towards Scalable and Deep Graph Neural Networks via Noise Masking
Yuxuan Liang, Wentao Zhang, Zeang Sheng, Ling Yang, Quanqing Xu, Jiawei Jiang, Yunhai Tong, Bin Cu
Towards Precise Prediction Uncertainty in GNNs: Refining GNNs with Topology-grouping Strategy
Hyunjin Seo, Kyusung Seo, Joonhyung Park, Eunho Yang
Spatio-Temporal Forecasting of PM2.5 via Spatial-Diffusion guided Encoder-Decoder Architecture
Malay Pandey, Vaishali Jain, Nimit Godhani, Sachchida Nand Tripathi, Piyush Rai
Graph 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 Zaitsev
Towards Effective Graph Rationalization via Boosting Environment Diversity
Yujie Wang, Kui Yu, Yuhong Zhang, Fuyuan Cao, Jiye Liang
Graph 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 Lai
BetaExplainer: A Probabilistic Method to Explain Graph Neural Networks
Whitney Sloneker, Shalin Patel, Michael Wang, Lorin Crawford, Ritambhara Singh
Accelerating 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 Xu
Concept Learning in the Wild: Towards Algorithmic Understanding of Neural Networks
Elad Shohama, Hadar Cohena, Khalil Wattada, Havana Rikab, Dan Vilenchik
GNNs-to-MLPs by Teacher Injection and Dirichlet Energy Distillation
Ziang Zhou, Zhihao Ding, Jieming Shi, Qing Li, Shiqi Shen