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
Analysis of Gene Regulatory Networks from Gene Expression Using Graph Neural Networks
Hakan T. Otal, Abdulhamit Subasi, Furkan Kurt, M. Abdullah Canbaz, Yasin Uzun
Graph Similarity Regularized Softmax for Semi-Supervised Node Classification
Yiming Yang, Jun Liu, Wei Wan
Higher-Order Message Passing for Glycan Representation Learning
Roman Joeres, Daniel Bojar
Topological Deep Learning with State-Space Models: A Mamba Approach for Simplicial Complexes
Marco Montagna, Simone Scardapane, Lev Telyatnikov
Metric-Semantic Factor Graph Generation based on Graph Neural Networks
Jose Andres Millan-Romera, Hriday Bavle, Muhammad Shaheer, Holger Voos, Jose Luis Sanchez-Lopez
Multi-Grid Graph Neural Networks with Self-Attention for Computational Mechanics
Paul Garnier, Jonathan Viquerat, Elie Hachem
Edge-Based Graph Component Pooling
T. Snelleman, B.M. Renting, H.H. Hoos, J.N. van Rijn
A Property Encoder for Graph Neural Networks
Anwar Said, Xenofon Koutsoukos
Uncertainty and Prediction Quality Estimation for Semantic Segmentation via Graph Neural Networks
Edgar Heinert, Stephan Tilgner, Timo Palm, Matthias Rottmann
High-Order Evolving Graphs for Enhanced Representation of Traffic Dynamics
Aditya Humnabadkar, Arindam Sikdar, Benjamin Cave, Huaizhong Zhang, Paul Bakaki, Ardhendu Behera
Can Graph Reordering Speed Up Graph Neural Network Training? An Experimental Study
Nikolai Merkel, Pierre Toussing, Ruben Mayer, Hans-Arno Jacobsen
GINTRIP: Interpretable Temporal Graph Regression using Information bottleneck and Prototype-based method
Ali Royat, Seyed Mohamad Moghadas, Lesley De Cruz, Adrian Munteanu
Contrasformer: A Brain Network Contrastive Transformer for Neurodegenerative Condition Identification
Jiaxing Xu, Kai He, Mengcheng Lan, Qingtian Bian, Wei Li, Tieying Li, Yiping Ke, Miao Qiao
Dynamic Fraud Detection: Integrating Reinforcement Learning into Graph Neural Networks
Yuxin Dong, Jianhua Yao, Jiajing Wang, Yingbin Liang, Shuhan Liao, Minheng Xiao
Flexible Diffusion Scopes with Parameterized Laplacian for Heterophilic Graph Learning
Qincheng Lu, Jiaqi Zhu, Sitao Luan, Xiao-Wen Chang
SAUC: Sparsity-Aware Uncertainty Calibration for Spatiotemporal Prediction with Graph Neural Networks
Dingyi Zhuang, Yuheng Bu, Guang Wang, Shenhao Wang, Jinhua Zhao
Sybil Detection using Graph Neural Networks
Stuart Heeb, Andreas Plesner, Roger Wattenhofer
Causal GNNs: A GNN-Driven Instrumental Variable Approach for Causal Inference in Networks
Xiaojing Du, Feiyu Yang, Wentao Gao, Xiongren Chen