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
AMOSL: Adaptive Modality-wise Structure Learning in Multi-view Graph Neural Networks For Enhanced Unified Representation
Peiyu Liang, Hongchang Gao, Xubin He
Graph Neural Networks Do Not Always Oversmooth
Bastian Epping, Alexandre René, Moritz Helias, Michael T. Schaub
DFA-GNN: Forward Learning of Graph Neural Networks by Direct Feedback Alignment
Gongpei Zhao, Tao Wang, Congyan Lang, Yi Jin, Yidong Li, Haibin Ling
Bayesian Mesh Optimization for Graph Neural Networks to Enhance Engineering Performance Prediction
Jangseop Park, Namwoo Kang
Graph Neural Network Enhanced Retrieval for Question Answering of LLMs
Zijian Li, Qingyan Guo, Jiawei Shao, Lei Song, Jiang Bian, Jun Zhang, Rui Wang
The Intelligible and Effective Graph Neural Additive Networks
Maya Bechler-Speicher, Amir Globerson, Ran Gilad-Bachrach
A hybrid numerical methodology coupling Reduced Order Modeling and Graph Neural Networks for non-parametric geometries: applications to structural dynamics problems
Victor Matray, Faisal Amlani, Frédéric Feyel, David Néron
Topology-Aware Dynamic Reweighting for Distribution Shifts on Graph
Weihuang Zheng, Jiashuo Liu, Jiaxing Li, Jiayun Wu, Peng Cui, Youyong Kong
LLM and GNN are Complementary: Distilling LLM for Multimodal Graph Learning
Junjie Xu, Zongyu Wu, Minhua Lin, Xiang Zhang, Suhang Wang
Graph Neural Network Training Systems: A Performance Comparison of Full-Graph and Mini-Batch
Saurabh Bajaj, Hojae Son, Juelin Liu, Hui Guan, Marco Serafini
Graph Neural Networks for Brain Graph Learning: A Survey
Xuexiong Luo, Jia Wu, Jian Yang, Shan Xue, Amin Beheshti, Quan Z. Sheng, David McAlpine, Paul Sowman, Alexis Giral, Philip S. Yu
Graph External Attention Enhanced Transformer
Jianqing Liang, Min Chen, Jiye Liang
Sheaf HyperNetworks for Personalized Federated Learning
Bao Nguyen, Lorenzo Sani, Xinchi Qiu, Pietro Liò, Nicholas D. Lane
SelfGNN: Self-Supervised Graph Neural Networks for Sequential Recommendation
Yuxi Liu, Lianghao Xia, Chao Huang
Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs
Langzhang Liang, Sunwoo Kim, Kijung Shin, Zenglin Xu, Shirui Pan, Yuan Qi
Heterophilous Distribution Propagation for Graph Neural Networks
Zhuonan Zheng, Sheng Zhou, Hongjia Xu, Ming Gu, Yilun Xu, Ao Li, Yuhong Li, Jingjun Gu, Jiajun Bu
GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning
Costas Mavromatis, George Karypis
FlexiDrop: Theoretical Insights and Practical Advances in Random Dropout Method on GNNs
Zhiheng Zhou, Sihao Liu, Weichen Zhao
Learning Latent Graph Structures and their Uncertainty
Alessandro Manenti, Daniele Zambon, Cesare Alippi