graph neural networks

A Review of Chest Imaging Report Generation Enhanced by Knowledge Graphs and Graph Neural Networks

Chest imaging report generation aims to automatically generate radiology reports from images such as chest X-rays and is an important task at the intersection of medical image analysis and natural language generation. In recent years, multimodal large language models have improved the fluency and organization of genera...

Study of Mainstream Methods in Stock Price Prediction: Performance Characteristics, Comparative Analysis and Fusion Innovation Directions

A primary insight is that future research should move towards integrating these complementary methodologies and leveraging multi-modal data sources, which holds the potential for developing more robust and economically interpretable predictive frameworks.

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

This paper proposes a heterogeneous graph attack chain reconstruction method based on LLM prior-knowledge guidance that verifies the effectiveness of the cooperation between semantic understanding and structural reasoning.

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

A graph-based anomaly detection model tailored for power generation systems using multivariate time-series data that incorporates both spatial and temporal dependencies is presented, demonstrating the advantages of incorporating both spatial and temporal dependencies.