graph neural networks

RoBCtrl: Attacking GNN-Based Social Bot Detectors via Reinforced Manipulation of Bots Control Interaction

Social networks have become a crucial source of real-time information for individuals. The influence of social bots within these platforms has garnered considerable attention from researchers, leading to the development of numerous detection technologies. However, the vulnerability and robustness of these detection met...

$$\varDelta $$-Net: Computing Tree Decomposition Using Monte Carlo Tree Search and Graph Neural Network

Multimodal Artificial Intelligence in Lung Cancer: From Data Integration to Precision Oncology

Simple Summary Lung cancer remains the leading cause of cancer-related deaths. Non-small cell lung cancer accounts for most cases, many of whom are on immunotherapy. Current clinical practice relies on a single biomarker, like PD-L1 and tumor mutation burden, to make decisions on immunotherapy treatments. However, both...

Revealing Seasonal Environmental Associations and Spatial Heterogeneity of Pacific Yellowfin Tuna CPUE Using an Interpretable Neural Network Framework

Understanding the spatial distribution patterns of pelagic species such as yellowfin tuna (Thunnus albacares) is essential for ecosystem-based fisheries management. However, characterizing CPUE–environment relationships remain challenging because these relationships may be nonlinear and spatially heterogeneous across l...

Feature-Personalized Graph Contrastive Clustering for ScRNA-Seq Data

Graph Neural Network and Transformer Fusion for Molecular Property Prediction: What a Strict Evaluation Protocol Finds

An evaluation protocol for multi-view molecular property prediction, and a fusion architecture measured under it. Eight MoleculeNet datasets, six scaffold splits (the DeepChem canonical split plus five seeded), paired comparisons with Holm correction and an across-dataset test, split-conformal uncertainty (LAC, APS, RA...

Graph Neural Network and Transformer Fusion for Molecular Property Prediction: What a Strict Evaluation Protocol Finds

An evaluation protocol for multi-view molecular property prediction, and a fusion architecture measured under it. Eight MoleculeNet datasets, six scaffold splits (the DeepChem canonical split plus five seeded), paired comparisons with Holm correction and an across-dataset test, split-conformal uncertainty (LAC, APS, RA...

Integrating Molecular Semantics and Three-Dimensional Geometry for Critical Property Prediction: A Multimodal GNN-BERT Framework

Accurate prediction of critical temperature, pressure, and volume is essential for thermodynamic modeling and process safety design, yet remains challenging for complex molecules under data-scarce conditions. Here, we develop a multimodal GNN-BERT framework that integrates SMILES-based chemical semantics with two-dimen...

Adversarial Robustness of Complex-Valued Graph Attention Beamformers: A Gradient Efficiency Study

Graph neural network (GNN) based beamformers have demonstrated near-optimal performance for multi-user MISO systems, yet their security under adversarial channel perturbations remains unexplored. We study the adversarial robustness of a complex-valued Graph Attention Network (GAT) beamformer trained via unsupervised La...

Spectral-temporal graph neural networks for demand forecasting on bipartite supply-customer graphs

Diese Masterarbeit untersucht, ob eine explizite Kunden–Produkt-Graphstruktur die monatliche industrielle Nachfrageprognose verbessern kann. Dafür wird ein TGGC-ähnliches spektral–temporales Graph Neural Network auf zwei Graphformulierungen angewendet: einen festen bipartiten Kunden–Site–Produkt-Graphen sowie einen lat...

Structurally Cascaded Physics‐Informed Graph Neural Networks for Mechanotransduction‐Aware Tumor Microenvironment Modeling

ABSTRACT Physics‐informed neural networks offer a useful mesh‐free approach for solving partial differential equations in complex multiphysics systems, such as the tumor microenvironment. These computational models can support future sustainable engineering applications and operations in precision medicine by enabling...

Jointly Predicting Civil Unrest Event Occurrence Probabilities and Potential Initiators via Multi-Task Learning

The eruption of civil unrest events often inflicts severe disruption on social stability. Accurate prediction of these events can assist decision-makers in proactively formulating countermeasures, thereby preventing violent incidents and mitigating property damage. Current event prediction methods often focus on predic...