graph neural networks

Dataset: Integrating Structural and Semantic Analysis for Code Smell Refactoring Prediction

The project bridges traditional static code analysis (Object-Oriented metrics) with advanced embedding representation learning to predict refactoring interventions on code-smelly methods. The predictive framework models a supervised binary classification task across 10 distinct target refactoring operations. Data Sourc...

Research data for "Online Decentralized Estimation of Network Connectivity in Cooperative Robotic Systems Using Graph Neural Networks"

This repository contains the datasets, analysis scripts, and experiment configuration used to generate the results presented in our research paper "Online Decentralized Estimation of Network Connectivity in Cooperative Robotic Systems Using Graph Neural Networks". This is a condensed release containing only the materia...

Camouflage-resistant graph neural networks for power grid anomaly detection

Power grid anomaly detection requires modeling both network topology and device dependencies. Graph neural networks (GNNs) are suitable for this task. However, few prior works have considered the stealthy camouflage behavior of grid anomalies, where faulty devices or malicious attackers intentionally mask their abnorma...

Computational Primes: A Systematic Framework for Partitioning Neural Network Computation Across Analog and Digital Domains

Twelve irreducible operations (P1-P12) with known physical implementations, and four without, as a basis for deciding which parts of a neural network belong in the analog domain. 114 algorithms are factored into these primes; the paper builds a compiler that decomposes a compute graph, assigns each prime to a domain, f...

Contrastive learning based multi-scale spatio-temporal-spectral network for patient-specific EEG seizure prediction

Accurate seizure prediction using electroencephalogram (EEG) signals is crucial for improving patients’ quality of life. However, the latent representations of preictal and interictal samples are difficult to effectively distinguish within the feature space. In this study, we propose a patient-specific seizure predicti...

PipeGNN: A Bandwidth-Efficient GNN Accelerator with Node-Level Pipelined Push Execution

PipeGNN is proposed, a bandwidth-efficient GNN accelerator whose streaming microarchitecture sustains this dataflow as a fully concurrent pipeline within a fixed on-chip buffer budget, and resolves four coupled microarchitectural barriers through coordinated mechanisms.

Nonlinearly Coupled Permafrost Dynamics From Graph Neural Networks

Toward accurate remaining useful life and state of health estimation using reinforced graph-based physics-informed neural networks enhanced with dynamic weights

Accurate estimation of Remaining Useful Life (RUL) and State of Health (SoH) is essential for reliable Prognostics and Health Management (PHM), supporting timely maintenance and dependable industrial operation. However, hybrid models that combine data-driven learning with physics-based regularization often rely on fixe...

Computational screening of oncogenic genetic variations in tumor suppressor proteins driving gastric cancer pathogenesis

Gastric cancer (GC) is currently the fifth most common cancer globally, often driven by dysregulation of tumor suppressor pathways. While individual studies on genetic variations of proteins are common, a comprehensive systems-level analysis of proteins regulating GC pathways showing both expression dysregulation and h...

Multimodal Spatiotemporal Neural Digital Twin Framework for Early Natural Disaster Prediction and Resilient Decision Support

Temporal graph anomaly detection based on multi-view semi-supervised learning

Multi-View Semi-Supervised Graph Anomaly Detection (MSGAD) is introduced, an innovative framework tailored for detecting anomalies in dynamic graph environments that enhances robustness against noise and unexpected events while improving sensitivity to subtle anomalous signals distributed across time.

Synthetic data-driven framework for estimating worker activity intensity using pose-based graph neural networks

This paper proposes a metabolic equivalent of task (MET) class prediction framework that combines Stable Diffusion-generated synthetic images with B-ResSkelGCN, which learns from skeleton graphs, for non-contact evaluation of construction workers' workload levels. Using domain-informed prompts, synthetic images were ge...