#graph neural networks
82 resultsCausalGraphX: A Counterfactual Graph Neural Network Framework for Explainable Systemic Risk Assessment
Rabimba Karanjai, Hemanth Madhavarao, Lei Xu +1
The paper presents CausalGraphX, a framework that combines graph neural networks with counterfactual reasoning to predict and explain systemic risk in financial networks, offering…
A Machine Learning Benchmarking Framework for Lipid Nanoparticle Transfection Efficiency Prediction
Asal Mehradfar, Mohammad Shahab Sepehri, Jose Miguel Hernandez-Lobato +4
The paper introduces a standardized machine‑learning benchmarking framework for predicting lipid‑nanoparticle transfection efficiency from ionizable lipid structures, evaluating ma…
Low-Latency Relay Selection in NR-V2X Vehicular Communications via Graph Isomorphism Networks with Edge Features
Giambattista Amati, Federica Mangiatordi, Emiliano Pallotti +3
The paper proposes a graph‑neural‑network approach (GINE) to select relay nodes for low‑latency NR‑V2X communications, using edge features and an offline MILP oracle to train the m…
Evaluation of State-of-the-Art Deep Learning Architectures for Aerodynamical Predictions
Jan Scherz, Derrick Hines, Philipp Bekemeyer
The paper benchmarks four modern deep learning operator learning models as surrogate solvers for aerodynamic predictions, evaluating their ability to predict surface pressure on 2D…
NodeImport: Imbalanced Node Classification with Node Importance Assessment
Nan Chen, Zemin Liu, Bryan Hooi +3
The paper proposes NodeImport, a framework that assesses node importance using a balanced meta-set to dynamically select valuable labeled, unlabeled, and synthetic nodes, improving…
Gauge-Invariant, Parameter-Insensitive Regularization for Potential Recovery from Flow on Directed Graphs
Mohammad Forouhesh
The paper proposes a gauge‑invariant regularization based on the graph Dirichlet energy to recover latent potentials from flow data on directed graphs, achieving stable estimates a…
Accelerating gas-network feasibility screening with a physics-informed graph neural network surrogate
Dongrui Jiang, Jochen Garcke, Okan Akca +4
The paper presents a physics‑informed graph neural network that quickly predicts steady‑state pressures and flows in large gas networks while enforcing mass‑balance, enabling rapid…
Implementations of Quantum and Classical Topology-Aligned Architectures for Molecular Property Prediction
James T. Pegg, Hubert Okadome Valencia, Ronin Wu
The paper introduces topology‑aligned architectures that map molecular bond graphs onto learnable units, implemented as a variational quantum circuit (Iso‑QGNN) and a matching clas…
DreamSat-Pose: Spacecraft Pose Estimation from Single-View 3D Reconstructions and Learned 2D-3D Feature Matching
Josiane Uwumukiza, Jocelyn Zhao, Giovanni Lavezzi +5
The paper introduces a single-shot framework that reconstructs a 3D shape of an unknown spacecraft from one image and then estimates its 6-DoF pose by learning dense 2D‑3D correspo…
CDS: Counterfactual Directionality Score for Structured Interventions in Spatial Graphs
Humaira Anzum, Md Ishtyaq Mahmud, Jagan Mohan Reddy Dwarampudi +1
The paper proposes a framework that uses structured counterfactual interventions on graph models to quantify directional influence between different node types, introducing the Cou…
GNN-based Online Beamforming Design for HAPS-Assisted NTN
Lavanya S S Anjapuli, Animesh Yadav, Halim Yanikomeroglu
The paper proposes using a high‑altitude platform station (HAPS) to relay data for cell‑edge users and designs beamforming vectors at both the terrestrial base station and HAPS via…
Variational Mixture of Graph Neural Experts for Alzheimer's Disease Recognition across Frequency Bands in EEG Brain Networks
Jun-En Ding, Anna Zilverstand, Shihao Yang +2
The paper introduces VMoGE, a variational mixture-of-experts model that uses graph neural networks to analyze EEG connectivity across multiple frequency bands for distinguishing Al…
GNN-DIP: Neural Corridor Selection for Decomposition-Based Motion Planning
Peng Xie, Yanliang Huang, Wenyuan Wu +1
The paper introduces GNN-DIP, a method that uses a graph neural network to score portals in a cell adjacency graph, guiding corridor selection for decomposition-based motion planni…
Beyond Parents? Prediction Gaps in University Completion Using Population-Scale Networks and Flexible Machine Learning
Javier Garcia-Bernardo, Eva Jaspers, Weverthon Machado +2
The paper investigates how much children’s university completion can be predicted from broader social contexts beyond parental background, using large administrative datasets and c…
Explaining Temporal Graph Neural Networks via Feature-induced Information Flow
Ping Xiong, Thomas Schnake, Klaus-Robert Müller +1
The paper introduces an attribution method that explains temporal graph neural networks by quantifying information flow through both event embeddings and event-induced variables, i…
StableAML: Machine Learning for Behavioral Wallet Detection in Stablecoin Anti-Money Laundering on Ethereum
Luciano Juvinski, Haochen Li, Alessio Brini
The paper presents a machine‑learning system that uses tree‑ensemble models to identify suspicious stablecoin wallets on Ethereum, achieving higher detection performance than graph…
Physics-Informed Structure Anchoring With Capture-Aware Prototype Calibration for Cross-Environment RF Fingerprinting
Fengchong Yao, Jianbing Li, Qing Liu +4
The paper introduces PISA-CAPC, a method that uses physics‑informed graph representations of antenna topology and carrier‑frequency‑offset conditioning, together with unlabeled pro…
Positional Attention-based Graph Neural Network for Learning Permutation Non-equivariant Wireless Policies
Baichuan Zhao, Chenyang Yang, Jianyu Zhao +1
The paper introduces a positional attention‑based graph neural network that can learn wireless policies which are not permutation equivariant, improving channel estimation and end‑…
RF-Informed Graph Neural Networks for Accurate and Data-Efficient Circuit Performance Prediction
Anahita Asadi, Leonid Popryho, Inna Partin-Vaisband
The paper introduces a graph neural network framework that uses RF-specific feature encoding to predict performance metrics of various active RF circuits with high accuracy and low…
Improving Molecular Property Prediction in Small Language Models Using Graph-based Tools
Konstantinos Bougiatiotis, Dimitrios Kelesis, Georgios Paliouras
The paper proposes a Context‑Augmented Prompting framework that lets small language models query a graph neural network expert for structural hints and explanatory subgraphs, impro…
STKAN: Kolmogorov-Arnold Networks for Spatio-Temporal Forecasting
Sicong Lai, Yuehong Hu, Siru Zhong +3
The paper introduces STKAN, a spatio‑temporal forecasting model that uses Kolmogorov‑Arnold network modules with Taylor‑polynomial approximations for spatial and temporal token mix…
EMAGN: Efficient Multi-Attention Graph Network via Learned Clustering for Scalable Traffic Forecasting
Mingxing Xu, Rakesh Chowdary Machineni, Ke Liu +7
The paper introduces EMAGN, a graph neural network that uses learned clustering to linearize multi-head attention, enabling scalable traffic forecasting with reduced computation an…
A multi-scale feature enhanced graph neural network for fluid dynamics prediction in complex geometries
Li Xiao, Tianyu Li, Yiye Zou +2
The paper introduces ME-GNN, a multi‑scale feature enhanced graph neural network that combines two‑step message passing, an Attention U‑Net, and K‑hop sampling to predict fluid flo…
Learning-based Multiuser Beamforming for Holographic MIMO~Systems
Shiyong Chen, Shengqian Han
The paper proposes a learning‑based approach using a gradient‑based graph neural network to design multi‑user beamformers for holographic MIMO systems, achieving higher spectral ef…