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 3
LLM Enhancers for GNNs: An Analysis from the Perspective of Causal Mechanism Identification
Hang Gao, Wenxuan Huang, Fengge Wu, Junsuo Zhao, Changwen Zheng, Huaping LiuChinese Academy of Sciences●National Key Laboratory of Space Integrated Information System●University of Chinese Academy of Sciences●Tsi...+1Hyperbolic Contrastive Learning with Model-augmentation for Knowledge-aware Recommendation
Shengyin Sun, Chen MaCity University of Hong Kong
The Correspondence Between Bounded Graph Neural Networks and Fragments of First-Order Logic
Bernardo Cuenca Grau, Przemysław A. WałęgaUniversityofOxford●QueenMaryUniversityofLondon●UniversityofŁódźTagging fully hadronic exotic decays of the vectorlike \mathbf{B} quark using a graph neural network
Jai Bardhan, Tanumoy Mandal, Subhadip Mitra, Cyrin Neeraj, Mihir RawatInternational Institute of Information Technology●Indian Institute of Science Education and Research ThiruvananthapuramGenerating Skyline Explanations for Graph Neural Networks
Dazhuo Qiu, Haolai Che, Arijit Khan, Yinghui WuAalborg University●Case Western Reserve UniversityBenchmarking Graph Neural Networks for Document Layout Analysis in Public Affairs
Miguel Lopez-Duran, Julian Fierrez, Aythami Morales, Ruben Tolosana, Oscar Delgado-Mohatar, Alvaro OrtigosaRepresentation Learning with Mutual Influence of Modalities for Node Classification in Multi-Modal Heterogeneous Networks
Jiafan Li, Jiaqi Zhu, Liang Chang, Yilin Li, Miaomiao Li, Yang Wang, Hongan WangChinese Academy of Sciences●University of Chinese Academy of Sciences●Beijing Normal University●Binzhou Institute of Technology
Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective
Henan Sun, Xunkai Li, Lei Zhu, Junyi Han, Guang Zeng, Ronghua Li, Guoren WangBeijing Institute of Technology●Jilin University●Ant GroupLearn to Think: Bootstrapping LLM Reasoning Capability Through Graph Learning
Hang Gao, Chenhao Zhang, Tie Wang, Junsuo Zhao, Fengge Wu, Changwen Zheng, Huaping LiuChinese Academy of Sciences●National Key Laboratory of Space Integrated Information System●University of Chinese Academy of Sciences●Pek...+2
SA-GAT-SR: Self-Adaptable Graph Attention Networks with Symbolic Regression for high-fidelity material property prediction
Junchi Liu, Ying Tang, Sergei Tretiak, Wenhui Duan, Liujiang ZhouUniversity of Electronic Science and Technology of China●University of Electronic Sciences and Technology of China●Los Alamos National...+2From GNNs to Trees: Multi-Granular Interpretability for Graph Neural Networks
Jie Yang, Yuwen Wang, Kaixuan Chen, Tongya Zheng, Yihe Zhou, Zhenbang Xiao, Ji Cao, Mingli Song, Shunyu LiuZhejiang University●State Key Laboratory of Blockchain and Data Security●Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data...+3Repetition Makes Perfect: Recurrent Sum-GNNs Match Message Passing Limit
Eran Rosenbluth, Martin GroheRWTH Aachen University●Funded by the German Research Council