graph neural networks

GraphCastTEC: a spherical multi-resolution graph neural network for global ionospheric total electron content forecasting

DHF-DTI: a dual-modality hybrid fusion framework for drug-target interaction prediction

Abstract Background Efficient computational methods for predicting drug–target interactions (DTI) are crucial for drug discovery, shortening the development cycle, and reducing costs. Although deep learning has achieved remarkable progress in this field, existing methods often struggle to fully exploit complementary in...

DSTG-Former: A Graph-Transformer for Intrusion Detection in DNP3 Smart Grids

The Distributed Network Protocol 3 (DNP3) remains the dominant communication standard in modern smart grids, yet it lacks intrinsic security, exposing supervisory control and data acquisition (SCADA) infrastructures to protocol-specific cyberattacks.Conventional intrusion detection systems (IDSs) rely on flow-level sta...

CGAF: Community-Guided Attentive Fusion for Community Detection in Multiplex Networks

Community detection in multiplex networks seeks groups of nodes from several types of relations. Attention mechanisms can integrate these relations, but weights based on embedding compatibility do not explicitly measure the community support provided by a node’s neighborhood. We propose Community-Guided Attentive Fusio...

Multi-stage attack detection in IIoT supply chains using graph neural networks

Abstract Industrial internet of things (IIoT) environments connect devices, gateways, hosts, and services in ways that increase both operational visibility and cyber-attack exposure. Conventional intrusion-detection systems commonly produce flat attack labels and may underuse relational information between communicatin...

Monte-Carlo methods for some challenging parameter estimation problems: intractable normalizing constants and differential privacy

This dissertation studies two statistical problems: Bayesian inference for network models with intractable normalizing constants and differentially private learning with stochastic neural networks.The first project develops noisy Langevin Monte Carlo for exponential random graph models. Auxiliary network simulations es...

Digital Twins In Drug Discovery, Development, And Personalized Therapeutics

DTs are well-established in the fields of aerospace and manufacturing engineering; however, their application in drug discovery and healthcare is rather poorly explored and lacks consensus on the definition. The present review synthesizes information from various sources throughout the stages of drug discovery and high...

TrustGuard-VehNet: An ensemble deep learning framework for trust modelling and malicious node detection in vehicular networks

Vehicular ad hoc networks (VANETs) require reliable trust assessment and attack detection to protect safety-critical communication under rapid topology changes. This paper presents TrustGuard-VehNet, an ensemble deep-learning framework that combines a one-dimensional convolutional neural network for short-window behavi...

Heterophily‐Aware Motif‐Guided Contrastive Learning for Robust Social Recommendation

ABSTRACT The homophily principle serves as the core foundation of graph neural network (GNN) based social recommendation, which assumes socially connected users share similar preferences and enables neighbourhood aggregation for user representation learning. Most existing methods directly aggregate information from ori...

Representation learning for low-data protein fitness prediction using sequence–structure embeddings and graph neural networks

Accurate prediction of mutational fitness effects remains challenging in low-data protein engineering settings, where experimentally characterized variants are limited. Although recent protein language models and structure-aware neural networks have demonstrated strong performance on large-scale datasets, the relative...

arpia-multi-scale-heterogeneous-gnn-mobile-device-geolocation

Code of the article ARPIA: A multi-scale heterogeneous graph neural network for mobile device geolocation with explicit non-connection modeling (L. F. C. Seelig and R. M. Salles, Ad Hoc Networks, 2026, doi:10.1016/j.adhoc.2026.104263): data enrichment, normalization, heterogeneous graph construction, the ARPIA model an...

Scenario-Aware Feeder Grouping with Transfer-Path Preservation for Post-Fault Load Restoration Using Graph Neural Networks

Post-fault load restoration in active distribution networks requires fast switching decisions that remain consistent with topology and operating constraints. Feeder grouping can accelerate restoration by simplifying weak inter-feeder connections, but a fixed grouping rule may either remove valuable transfer paths or le...