graph neural networks

Prediction of supercritical CO2 solubility with a thermodynamics-conditioned equivariant graph neural network model

Dynamic knowledge graph and graph neural network-based early-warning method

Early warning of corporate debt default puts forward higher requirements for multi-source credit data access, relationship risk identification and early warning level judgment. Aiming at the problems of lagging credit indicators, insufficient description of relational risks and inadequate utilization of external events...

A graph neural network-based method for enterprise network lateral movement detection

A hierarchical attention-based graph neural network is introduced to address the challenge of detecting lateral movement within large-scale enterprise networks. By representing users, hosts, and endpoint devices as nodes and encoding authentication and communication events as edges, the proposed framework captures both...

Leakage-Controlled Subject-Aware Graph-Temporal Attention Network (SGTA-Net) for EEG-Based Emotion Recognition

EEG-based emotion recognition results may be artificially inflated due to data leakage from normalization, augmentation, or validation splits. This article introduces SGTA-Net, a Subject-Aware Graph-Temporal Attention Network, evaluated on the DEAP dataset using only EEG features. The approach incorporates feature-spac...

GRAPH NEURAL NETWORK-BASED CLUSTERING OF FLOOR PLANS VIA VISUAL SEGMENTS

Mid-level segments of spatial articulation exist between individual points and the spatial whole. Based on visibility graphs, these can be defined as segments analogous to graph-theoretic communities. This paper proposes a method for clustering such segments using Graph Neural Network-based Invariant Information Cluste...

Conservation laws for explainability in spiking neural networks

Abstract Explainability in event-driven dynamical models such as Spiking Neural Networks (SNNs) is often adapted from methods developed for static networks, even though the explanation itself is defined over a spatio-temporal computation graph. We present a conservation-law interpretation of Layer-wise Relevance Propag...

Benchmarking Graph Neural Networks in Solving Hard Constraint Satisfaction Problems

Code, benchmarks and results for Benchmarking Graph Neural Networks in Solving Hard Constraint Satisfaction Problems (arXiv:2602.18419).

Physics-Informed Graph Neural Network Surrogate for Operational Estimation of Wake-Induced Power Losses in Wind Farms

Limited land packs wind turbines a few rotor diameters apart as electricity demand continues to rise. Wind already supplies about 10% of U.S. electricity and 23% of Texas ERCOT generation. Each rotor leaves a wake of slower, more turbulent air. Power scales with the cube of wind speed, so a 15% speed drop is about a 39...

Quantum machine learning: A comprehensive survey of models, tools, and challenges

Abstract Quantum Machine Learning (QML) is an emerging interdisciplinary field that merges quantum computing with classical machine learning (ML) to enhance computational efficiency, scalability, and accuracy in data-driven tasks. By leveraging quantum properties such as superposition, entanglement, and quantum paralle...

From Heuristics to Learning a Review of AI Methods for Logic Gate in EDA

Description This review paper examines how Artificial Intelligence is changing logic gate synthesis in Electronic Design Automation (EDA). Classical heuristics such as Karnaugh maps, Quine-McCluskey and AIG rewriting struggle to scale to circuits with billions of gates, especially when balancing power, performance and...

THE PRECISION BIO-INTELLIGENCE FRAMEWORK FOR COMBATING MRSA AND PERSONALIZED MEDICINE FROM MOLECULAR DIAGNOSTICS TO GENE-EDITING THERAPIES A Unified Theory of Antimicrobial Resistance, Ultra-Early Detection, AI-Driven Drug Discovery, Personalized Genetic and Cellular Therapy, and Health Law

The Precision Bio-Intelligence Framework for Combating MRSA and Personalized Medicine by Dr. M.K.A. El-Rakhawi (DOI: 10.5281/zenodo.23462676) establishes Precision Bio-Intelligence as a groundbreaking field integrating antimicrobial resistance, ultra-early detection, AI-driven drug discovery, genetic/cellular therapy,...

Deep learning-based research on cultural symbols in few-shot destination social media images

In the digital age, cultural heritage sites utilise social media imagery for marketing. Nevertheless, existing research reveals two gaps: the scarcity of methods for accurately identifying multi-cultural element fusion in images under few-shot conditions; and the difficulty in quantifying their collective impact on use...