graph neural networks

Privacy-Preserving Porosity Prediction Using Spatiotemporal Graph Neural Networks in Laser-Based Additive Manufacturing Processes

Porosity control in the LENS (Laser Engineered Net Shaping) additive manufacturing process is critical for ensuring structural integrity and durability, especially in high-performance applications. Traditional predictive models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), often st...

Multimodal molecular representation and property prediction based on structured complementary feature fusion

In this work, we propose Molrep-SCF, a multimodal self-supervised pretraining framework for molecular representation learning using SMILES sequences, 2D molecular graphs, and 3D geometric structures. The three modalities are encoded using a Transformer, a graph neural network, and an invariant 3D graph neural network,...

FraudGraph-X: Privacy-Preserving Temporal Graph Learning for Scalable and Explainable Financial Fraud Detection

Context-Aware Graph Neural Networks (CAGNN) for Multimodal Prediction of Parkinson’s Disease Dementia

Parkinson’s disease dementia is a severe cognitive decline in up to 95% of Parkinson’s disease sufferers within 10–20 years after diagnosis. Diagnosis relies on comprehensive clinical evaluation; however, research indicates that underlying physiological changes to white matter tracts precede symptomatic presentation. E...

Mechanistically informed multi-scale spatiotemporal autoregressive graph learning reveals cascading impacts of extreme precipitation

Economic losses caused by extreme climate are not confined to the locations where events occur, but can propagate across regions through physical and economic linkages. Yet existing climate-impact assessment methods remain poorly suited to tracing how shocks spread across space and reshape the geography of economic los...

Graph Neural Network‐Guided and Interpretable Discovery of Supercapacitor Electrode Materials

Discovering electrode materials that combine high capacitance, stability, conductivity, and practical synthesis remains a significant challenge for advancing supercapacitors beyond the energy‐density limits of traditional carbon‐based systems. Here, we introduce an AI‐driven framework integrating graph neural netwo...

Physics-Informed Machine Learning for Fatigue and Fracture Analysis and Prediction: A Scoping Review

Physics-Informed Machine Learning has emerged as a powerful paradigm for fatigue and fracture analysis, combining the data-driven flexibility of neural networks with the consistency of physical laws. This scoping review maps the extent, range, and nature of research on PIML applied to fatigue life prediction, crack gro...

Enhancing electromagnetic calorimeter signal reconstruction with machine learning-based noise discrimination

Abstract Calorimeters operating in high-radiation environments are susceptible to damage, leading to increased noise that can significantly degrade energy resolution. A common way to mitigate noise is to apply a higher energy threshold on the calorimeter cells, typically set a few standard deviations above the noise le...

Collaborative trust computation model for social internet of things

Trust compromise is a major problem in social internet of things (SIoT). Existing trust management approaches often rely on predefined trust values, limited behavioral attributes, or historical interactions, making them less effective under dynamic and cold-start environments. This paper proposes a deep collaborative e...

A novel physics-guided graph transformer (PHY-GT) for multimodal brain tumor classification

Abstract Background Accurate classification of brain tumors from magnetic resonance imaging (MRI) is critical for effective clinical diagnosis and treatment planning. Although deep learning models such as convolutional neural networks and vision transformers have demonstrated strong performance, challenges remain in ha...

A Comparative Analysis of Power-Delay-Area Improvements Between Traditional Heuristic and AI-Driven Electronic Design Automation Techniques

This paper presents a conceptual literature review and qualitative comparative synthesis of two major families of Electronic Design Automation (EDA) logic-optimization techniques: modern classical heuristic logic synthesis, grounded in scalable multi-level DAG-based optimization frameworks (Mishchenko et al., 2018; Ama...

A Comparative Analysis of Power-Delay-Area Improvements Between Traditional Heuristic and AI-Driven Electronic Design Automation Techniques

This paper presents a conceptual literature review and qualitative comparative synthesis of two major families of Electronic Design Automation (EDA) logic-optimization techniques: modern classical heuristic logic synthesis, grounded in scalable multi-level DAG-based optimization frameworks (Mishchenko et al., 2018; Ama...