graph neural networks

Code for: Selecting Descriptor Models and Graph Neural Networks For Structural Response Prediction Of Varying Geometric Complexity

This paper compares descriptor models and geometric point graph neural networks for structural response prediction in procedurally generated L-shaped brackets with zero to eight holes. Complexity is quantified using hole count, removed-area fraction, boundary multiplier, inverse compactness and normalized minimum ligam...

Compositional Quantum Heuristics for Max-Clique Detection

Abstract Quantum machine learning holds the promise of combining the success of classical machine learning methods with the power of quantum computing, however one of the largest obstacles facing the field is the problem of barren plateaus. Parameterised quantum circuits offer a flexible framework for developing quantu...

Cross-Document Neural Re-ranking via Query-Induced Subgraphs

AI-Powered Logic Gate Optimization for Efficient Electronic Design

Abstract The continuous development of modern electronic devices has resulted in increasingly complex Integrated Circuits (ICs). As the number of logic gates and circuit components increases, designing efficient electronic systems becomes more challenging. Engineers need to consider several important factors, including...

AI-Powered Logic Gate Optimization for Efficient Electronic Design

Abstract The continuous development of modern electronic devices has resulted in increasingly complex Integrated Circuits (ICs). As the number of logic gates and circuit components increases, designing efficient electronic systems becomes more challenging. Engineers need to consider several important factors, including...

Protein Language Model-Conditioned Graph Neural Networks for Multitask GPCR Ligand Activity Prediction

Predicting ligand activity across G protein-coupled receptors (GPCRs) requires models that capture both molecular structure and receptor-specific information while remaining robust to chemical and target-domain shifts. We developed a multimodal graph neural network that combines explicit ligand molecular graphs with fr...

Artificial Intelligence Reveals How Alloying Amount Turns Electronic Structure into Mechanical Performance

This repository provides the complete computational workflow for AlloyGCN-based prediction, surrogate modeling, and explainable analysis of Cantor alloys with additional alloying elements. The pipeline includes: Graph neural network (AlloyGCN) training and evaluation for process-dependent mechanical property prediction...

Graph neural networks guide progressive metric updates for congestion free OSPF reconfiguration

Graph Neural Network Guided Progressive Metric Updates (GNN-PMU), a graph-aware scheduler that ranks metric changes using topology, utilization, queue, capacity, and pending-update information, is presented.

Spectral network determinants of seizure-like synchronization and spread in coupled Hindmarsh–Rose brain models

An intelligent cybersecurity framework for banking systems: integrating neural networks, large language models, and federated learning for anti-money laundering and fraud detection

The fraud of money laundering costs the global financial system USD 800 billion to USD 2 trillion annually, while digital banking contributes to the increasing number and complexity of money laundering transactions. If there are adversarial forces that are constantly adapting their approach to avoid complying with a co...

Synthetic false data injection-inspired perturbation framework for power grid attack detection using graph neural networks and deep learning models

The novelty of this work lies in providing a unified, leakage-free comparative framework spanning graph- and non-graph-based deep learning architectures under identical experimental conditions, which provides a realistic and extensible benchmark for future power-grid cybersecurity research.

UltraGNN: A Sparse-Operator-Aware Framework for Accelerating Graph Neural Networks on Tensor Cores

Graph Neural Networks (GNNs) have achieved widespread success from social networks to AI-for-Science. Most existing GNN frameworks adopt scatter-first (edge-centric) or gather-first (vertex-centric) scheduling paradigms for message passing. However, these paradigms are closely tied to traditional CUDA-core execution mo...