graph neural networks

Agent-Based Simulation and Eleven-Stage ML Pipeline Code for Climate-Adaptive Practice Adoption Among Smallholder Farmers (Zone K)

Quantifying Social Diffusion in Climate-Adaptive Practice Adoption: An Agent-Based Simulation and Explainable Machine Learning Framework for Smallholder Farmers." It contains the agent-based simulation generator and the complete eleven-stage analysis pipeline used to produce every table, figure, and reported statistic...

NED-HGT: a node-edge decoupled heterogeneous graph transformer for accurate solubility prediction

Measured multi-source fusion graph neural network for semi-supervised imbalanced working condition recognition

Topology Conditioned Physics Informed Graph Surrogates for AC Optimal Power Flow Under Line Outages

Security assessment requires repeated alternating current optimal power flow (AC OPF) solutions under changing injections and line outages. We propose a topology conditioned physics informed graph Fourier neural operator (GFNO PINO) that predicts voltage phasors and dispatch from network and operating data. Chebyshev f...

Data-driven prediction of total ionization cross sections for small molecules using graph neural networks

Abstract Total ionization cross sections (TICS) for electron impact on molecules are essential inputs for plasma modeling and electron-transport simulations. The semi-empirical Binary-Encounter-Bethe (BEB) method provides reliable single-ionization TICS, but every molecule requires a separate quantum-chemical calculati...

A Hybrid Learning Framework for Automated SAT Solver Selection

Boolean Satisfiability (SAT) solving underlies a wide range of practical applications, including hardware verification, software testing, automated planning, and combinatorial design. Decades of engineering effort have produced highly optimised Conflict-Driven Clause Learning (CDCL) solvers, yet empirical studies consi...

From Computational Chemistry to Generative Models: A Survey of AI-Driven Small-Molecule Drug Discovery

Small-molecule drug discovery requires navigating enormous chemical space to identify candidates that are simultaneously potent, selective, synthetically accessible, and acceptable across pharmacokinetic and safety profiles; modern generative AI extends a long computational medicinal chemistry lineage that began with Q...

Fair Graph Learning Needs Expressiveness: Rethinking Fairness from the Spectral Perspective

It is formally proved that GNN architectures lacking spectral expressiveness impose strict constraints on the representation space, so that harmful linear correlations between sensitive attributes and target prediction logits are preserved whenever a low-expressive backbone is paired with a debiasing operator acting wi...

Reproducibility Package for What Information Can Be Discarded

Release purpose This archive is the release-grade executable evidence and verification system for “What Information Can Be Discarded?” It freezes the proof-support artifacts, experiment suites, source code, public or generated inputs, protocols, environment locks, sealed results, statistical analyses, and paper-to-resu...

Trajectory-Aware Intelligent Satellite Handover for Power Systems via Graph Neural Networks and Deep Reinforcement Learning

Facing the power communication needs in remote areas lacking public network coverage, Low Earth Orbit (LEO) satellite networks are considered an important support for power communication due to their advantages of wide coverage, low latency, and high bandwidth. However, the high speed of the satellites leads to frequen...

Deep Learning-Based Intelligent Early Warning System for Corporate Financial Risk

With the advancement of computer technology, corporate financial risk control systems face challenges related to complex data processing and delays in risk identification. This paper proposes an intelligent risk control method based on a hybrid LSTM-GNN (Long Short-Term Memory-Graph Neural Network) model, utilizing LST...

A Hybrid Learning Framework for Automated SAT Solver Selection

Boolean Satisfiability (SAT) solving underlies a wide range of practical applications, including hardware verification, software testing, automated planning, and combinatorial design. Decades of engineering effort have produced highly optimised Conflict-Driven Clause Learning (CDCL) solvers, yet empirical studies consi...