graph neural networks

Advancing Soil Assessment Quality for Crop Yield Prediction Using an Optimized Category Integrated Dual Task Graph Neural Network Approach

Soil quality assessment plays a role in improving agricultural productivity and sustainability, as it is essential for making informed decisions in precision farming. This study proposes a new model for soil assessment quality (SAQ) and crop yield prediction (CYP) based on a category integrated dual task graph neur...

A dynamic graph convolutional network with multiscaled attention for traffic prediction

This paper aims to propose attention-based dynamic graph convolutional recurrent neural network (ADGCRNN) for highway traffic flow prediction, which outperforms state-of-the-art baseline models and realizes multiresolution temporal fusion via self-attention.

A graph neural network surrogate for traffic simulation in probabilistic wildfire evacuation planning

Agent-based traffic simulation is one of the main computational bottlenecks in probabilistic wildfire evacuation frameworks that rely on Monte Carlo analysis to populate Bayesian network models. This paper presents a graph neural network (GNN) surrogate that replaces it within the WiSE (Wildfire Safe Egress) framework....

An interpretable deep learning framework for classifying neuronal morphologies using topology and graph neural networks

Abstract Neuronal shape determines how neurons process and integrate information, yet a consistent and objective classification of neuronal morphologies remains elusive. Current approaches rely heavily on subjective expert views or on predefined features, limiting reproducibility and interpretability. Here, we present...

Wavelet-enhanced Mamba and graph network-based model for time series anomaly detection

Link Prediction in Networked Data Using Structural Features and Machine Learning

It is indicated that structural information remains important for link prediction in contemporary learning-based models and future work will continue to improve the scalability, robustness, temporal adaptation and interpretability of link prediction models for large-scale, dynamic real-world networks.

Attack Chain Reconstruction Method Based on LLM Prior-Knowledge Guidance and Heterogeneous Graph Reasoning

In the face of multi-stage and cross-device complex network attacks, reconstructing the complete attack link from massive heterogeneous security logs is the core problem of security operation. In the existing methods, the traditional graph model lacks a deep understanding of the semantics of alerts, and large language...

Modeling of spatial–temporal dynamic dependency in traffic data to predict its evolution

Accurate traffic forecasting enables efficient traffic management. However, traffic prediction is a challenging task as the transportation system itself presents complex dynamic characteristics due to the complex interactions of multiple agents (such as randomly mixed vehicles with different mechanical characteristics,...

38. INTEGRATIVE MULTI-OMICS AND DEEP LEARNING ANALYSIS IDENTIFIES CTNND2 AND NETO1 AS KEY NEURODEVELOPMENTAL DISORDER RISK GENES IN A PEDIATRIC COHORT OF 68,975 INDIVIDUALS

Background Neurodevelopmental disorders (NDDs) are highly heterogeneous psychiatric conditions with complex genetic architectures and limited mechanistic understanding. Although genome-wide association studies (GWAS) have identified multiple susceptibility loci, translating these findings into biological insight and th...

Graph-enhanced bidirectional GRU for anomaly detection in power generation environments

This paper presents a graph-based anomaly detection model tailored for power generation systems using multivariate time-series data. The model integrated a bidirectional gated recurrent unit with graph neural networks and graph attention networks to effectively capture temporal dynamics and spatial relationships among...

Detecting communities in social networks with graph convolutional neural networks

The latent community structures in the social networks have now become a cornerstone problem in the scientific study of networks, and has extensive implications in recommendation systems, epidemiology, fraud detection, and social behavior studies. The conventional community detection algorithms, most of which are based...

Power Bond graph-embedded causal flow-aware graph neural network for fault localization of electro-hydraulic systems

To achieve precise fault localization for electro-hydraulic systems of mechanical, a Causal Flow-aware Graph Neural Network (CFAGNN) is proposed, which integrates power bond graph topology with deep learning. A power bond graph model is constructed to characterize energy transfer mechanisms and clarify dynamic coupling...