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
Supra-Laplacian Encoding for Transformer on Dynamic Graphs
Yannis Karmim, Marc Lafon, Raphaël Fournier S'niehotta, Nicolas Thome
Convolutional Signal Propagation: A Simple Scalable Algorithm for Hypergraphs
Pavel Procházka, Marek Dědič, Lukáš Bajer
On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks
Jiong Zhu, Gaotang Li, Yao-An Yang, Jing Zhu, Xuehao Cui, Danai Koutra
MCI-GRU: Stock Prediction Model Based on Multi-Head Cross-Attention and Improved GRU
Peng Zhu, Yuante Li, Yifan Hu, Sheng Xiang, Qinyuan Liu, Dawei Cheng, Yuqi Liang
Physics-Informed Graph-Mesh Networks for PDEs: A hybrid approach for complex problems
Marien Chenaud, Frédéric Magoulès, José Alves
Erase then Rectify: A Training-Free Parameter Editing Approach for Cost-Effective Graph Unlearning
Zhe-Rui Yang, Jindong Han, Chang-Dong Wang, Hao Liu
GraphLoRA: Structure-Aware Contrastive Low-Rank Adaptation for Cross-Graph Transfer Learning
Zhe-Rui Yang, Jindong Han, Chang-Dong Wang, Hao Liu
Pre-trained Graphformer-based Ranking at Web-scale Search (Extended Abstract)
Yuchen Li, Haoyi Xiong, Linghe Kong, Zeyi Sun, Hongyang Chen, Shuaiqiang Wang, Dawei Yin
Towards Explainable Graph Neural Networks for Neurological Evaluation on EEG Signals
Andrea Protani, Lorenzo Giusti, Chiara Iacovelli, Albert Sund Aillet, Diogo Reis Santos, Giuseppe Reale, Aurelia Zauli, Marco Moci, Marta Garbuglia, Pierpaolo Brutti, Pietro Caliandro, Luigi Serio
GraphGI:A GNN Explanation Method using Game Interaction
Xingping Xian, Jianlu Liu, Tao Wu, Lin Yuan, Chao Wang, Baiyun Chen
Unveiling the Potential of Graph Neural Networks in SME Credit Risk Assessment
Bingyao Liu, Iris Li, Jianhua Yao, Yuan Chen, Guanming Huang, Jiajing Wang
FastGL: A GPU-Efficient Framework for Accelerating Sampling-Based GNN Training at Large Scale
Zeyu Zhu, Peisong Wang, Qinghao Hu, Gang Li, Xiaoyao Liang, Jian Cheng
Learning to Simulate Aerosol Dynamics with Graph Neural Networks
Fabiana Ferracina, Payton Beeler, Mahantesh Halappanavar, Bala Krishnamoorthy, Marco Minutoli, Laura Fierce
A Generative Framework for Predictive Modeling of Multiple Chronic Conditions Using Graph Variational Autoencoder and Bandit-Optimized Graph Neural Network
Julian Carvajal Rico, Adel Alaeddini, Syed Hasib Akhter Faruqui, Susan P Fisher-Hoch, Joseph B Mccormick