graph neural networks

A study on hybrid graph neural network-based approach for link prediction

Purpose The purpose of the presented study is to explore graph and analyse the performance of hybrid graph neural networks for link prediction tasks in a graph. Graphs are a collection of nodes and edges that form a kind of network and represent relationships. Link prediction in graphs plays a vital role in various app...

Human behavior recognition based on graph neural network and cross-modal collaborative attention mechanism

Multimodal Deep Learning and Knowledge Graph Framework for Early Disease Screening

Abstract — Traditional artificial intelligence (AI) systems often treat medical images and clinical data separately. However, screening various diseases can facilitate their early detection and prompt clinical action. Decoupled handling of medical images and clinical data could potentially limit AI’s ability to associa...

NoiTox: A Machine Learning Strategy for Peptide Toxicity Prediction Reveals Label Noise in Benchmark Datasets

Motivation Peptide toxicity remains a major bottleneck in drug development, as small sequence variations can drastically alter cytotoxic profiles. Experimental assessment is costly and time-consuming, motivating computational predictors. Existing approaches often rely on single black-box architectures, limiting interpr...

Local Citation Recommendation via Heterogeneous Graph Neural Networks and Contrastive Learning

Spatiotemporal Machine Learning for Smart Cities: A Comprehensive Review of Models and Applications

The rapid growth of sensor networks, satellite platforms, Internet-of-Things (IoT) devices, and urban monitoring systems has produced vast volumes of spatiotemporal data. Modeling such data is central to applications ranging from air-quality forecasting and climate monitoring to traffic prediction and urban planning, y...

Continual Lifelong Learning in Foundation Models: Mitigating Catastrophic Forgetting, Model Editing, and Dynamic Modular Expansion

Abstract—Modern foundation models, particularly autoregressive Large Language Models (LLMs) and Vision-Language Models (VLMs), operate predominantly under a static pre-train-then-freeze deployment paradigm. However, real-world enterprise deployments demand continuous, lifelong adaptation to non-stationary data streams:...

Mittels Knoten und Kanten zu Resilienz: Einsatz von Graph Neural Networks für Wertschöpfungsketten in Niedersachsen

Stock Forecasting Using Sequential Models and GNN

Forecasting stock prices, market trends, and associated emotions remains a complex challenge due to the market’s inherent volatility, nonlinearity, susceptibility to factors such as news events, and the limited availability of financial data. Stock prices are noisy, unpredictable, and sensitive to investor sentiment. T...

Causal Invariant Bio-Inference: Breaking the Observational Barrier in Rhizosphere Dynamics via Physically-Constrained Edge-GNN Structural Causal Models

Observational machine learning models (e.g., Transformers, CNNs, Random Forests) fit joint probability distributions P(Y|X) and inherently break down out-of-distribution when spurious correlations fail under environmental volatility. In rhizosphere dynamics, unobserved confounders and micro-climatic shifts frequently l...

Graph Neural Networks (GNNs) for Complex System Anomaly Detection and Cybersecurity Telemetry: Topologies, Temporal Dynamics, and Enterprise SOC Benchmarks

Abstract—Modern enterprise computing fabrics, cloud infrastructures, and Security Operations Centers (SOCs) are inundated by billions of security events daily. Conventional Security Information and Event Management (SIEM) engines and intrusion detection systems (IDS) analyze telemetry as isolated, flat tabular logs (e....

ALGORITHMIC PHYSICAL INTELLIGENCE: ENGINEERING BIO-DIGITAL HYBRID COMPUTING FROM FIRST PRINCIPLES

Algorithmic Physical Intelligence (API) introduces a paradigm-shifting framework for bio-digital hybrid computing from first principles, addressing the fundamental thermodynamic barrier of Landauer's Limit in silicon-based systems. The El-Rakhawi Hybrid Architecture integrates three novel modules: AI-guided molecular l...