graph neural networks

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...

Integrating Metagenomics and Deep Learning for Accurate Microbial Species Differentiation

Accurate identification of microbial species is important for clinical diagnosis, environmental monitoring, and evaluations in agriculture and biotechnology. Conventional culture- and alignment-based metagenomic methods have several drawbacks, including inadequate reference databases, the cost of digital compilations,...

AI-Driven Intrusion Detection for Vehicular Networks: A Comprehensive Survey of Techniques, Datasets, Deployment Architectures, and Future Directions

Intelligent transportation systems (ITSs), Vehicle-to-Everything (V2X) communication and autonomous driving technologies have brought about significant changes in the modern vehicular network. At the same time, the cyber-attack surface has grown, leading to new and existing advanced security threats for Vehicular Ad ho...

Graph Learning with Multiplicative Aggregation for Solar Filament Fragment Association Modeling in Full-disk H α Images

Abstract Solar filaments are prominent features in H α observations. During their evolution, they often exhibit complex morphological and topological changes, which, due to line-of-sight projection effects, appear as fragmentation and merging. These phenomena make it difficult to reliably track solar filaments using au...

DrugSAGE: a transcriptome aggregation approach using cell lines for drug response imputation

Accurate drug response prediction is essential for optimizing cancer therapy, yet genomic heterogeneity drives variable responses even among tumors with identical driver mutations. We developed DrugSAGE, a Graph Neural Network framework that predicts drug response from transcriptomic data by aggregating features from e...

Graph neural network-based algorithm for design optimization of truss structures

A novel Graph Neural Network (GNN)-based algorithm for design optimization of truss structures is proposed in this paper presents through a combination of Multi-head Attention (MHA) mechanism and a newly developed hybrid adaptive sampling strategy. The algorithm involves the generation of high-fidelity training dataset...

Evolution of deep learning for structural magnetic resonance imaging analysis in Alzheimer’s disease: from slice-level classification to brain structure modeling

Structural magnetic resonance imaging (sMRI) depicts Alzheimer’s disease (AD)-related atrophy noninvasively and has become a common input for deep-learning studies of diagnosis and progression. For this structured narrative Mini Review, we searched PubMed/MEDLINE, the Web of Science Core Collection, and IEEE Xplore thr...

phani1228/Causal_Temporal_Graph_Reproducibility: Reproducibility Package for Snapshot-Aligned Dynamic Multiplex Stock-Graph Construction

Dynamic Graph, Multiplex Graph, Stock Market, NIFTY 500, Graph Neural Network, Financial Networks, Market Correlation, Financial Similarity, Macroeconomic Sensitivity, Reproducibility README.md requirements.txt

Modular constraint-based joint dynamic functional brain graph learning model for identifying brain disorders

Resting-state functional magnetic resonance imaging (rs-fMRI) captures spontaneous neural activity and has become a valuable tool for investigating functional alterations in brain disorders. A widely used strategy is to estimate functional connectivity (FC) between brain regions based on blood-oxygen-level-dependent (B...

GREAF: Graph-based Real-time Anomaly Detection via Forecasting for IoT-Enabled Indoor Spaces

The increasing deployment of IoT sensors in smart homes and offices enables data-driven automation while exposing these systems to sensor faults, abnormal user activities, and unexpected event patterns. Graph Neural Networks (GNNs) offer a principled way to model spatial dependencies among sensors, yet existing GNN-bas...

A financial fraud detection and explainability analysis framework based on propagation influence quantification and the integration of heterogeneous graph neural networks

Intelligent Diagnosis Method for Rolling Bearings Based on Multi-source Information Fusion and Adaptive Graph Neural Network