Evaluating Directed Acyclic Graphs with DAGMetrics: Insights from Tuber and Soil Microbiome Data by Pavel Averin, Ifigeneia Mellidou, Maria Ganopoulou, Aliki Xanthopoulou, Theodoros Moysiadis

“…Understanding and evaluating directed acyclic graphs (DAGs) is crucial for causal discovery, particularly in high-dimensional and small-sample datasets such as microbial abundance data. …”
Get full text

IRGL-RRI: interpretable graph representation learning for plant RNA–RNA interaction discovery by Qingquan Liao, Xuchong Liu, Wei Zhao, Yu Tong, Fangzheng Xu, Xinxin Liu, Yifan Chen

“…A graph representation based on a masking strategy and regularization enhances RNA feature extraction. …”
Get full text

Comparative Analysis of Multi-Omics Integration Using Graph Neural Networks for Cancer Classification by Fadi Alharbi, Aleksandar Vakanski, Boyu Zhang, Murtada K. Elbashir, Mohanad Mohammed

“…Differential gene expression and LASSO (Least Absolute Shrinkage and Selection Operator) regression are employed for reducing the omics data dimensionality and feature selection; hence, the developed models are referred to as LASSO-MOGCN, LASSO-MOGAT, and LASSO-MOGTN. Graph structures constructed using sample correlation matrices and protein-protein interaction networks are investigated. …”
Get full text

Graph Representation Learning for the Prediction of Medication Usage in the UK Biobank Based on Pharmacogenetic Variants by Bill Qi, Yannis J. Trakadis

“…A graph convolutional network (GCN) was used to integrate interconnected biomedical entities in the form of a knowledge graph as part of a machine learning (ML) prediction model. …”
Get full text

Topology-Guided Graph Masked Autoencoder Learning for Population-Based Neurodevelopmental Disorder Diagnosis by Yueying Li, Xiaotong Zhang, Shihan Guan, Guolin Ma, Youyong Kong

“…Existing methods either combine clinical information to assist analysis or use data augmentation for sample expansion, ignoring the mining of individual information and the exploration of inter-individual associations in population. …”
Get full text

Unveiling Hidden Structures: Multi-Platform IoT Malware Analysis Using Graph Embeddings by Remus M. Petrache, Ciprian Oprisa, Camelia Lemnaru

“…We explore three different methods for generating graph embeddings-at node, edge and graph level-and find the latter to produce the best discrimination between malware families across different platforms. …”
Get full text

Neighborhood Information Aggregation and Multi-View Feature Extraction-Based Contrastive Graph Clustering by Liulong Yao, Jinrong Cui, Yazi Xie, Chengli Sun

“…In addition, we design a weight graph that allows the model to adaptively adjust the proximity between samples during the learning process. …”
Get full text

Dynamic Graph Attention Network with Sentiment Analysis for Fake News Detection in Social Networks by Fatemeh Jokar

“…It includes a graph construction module that updates the graph based on temporal data and a graph attention module that assigns importance to nodes and edges within the graph. …”
Get full text

TQAgent: Enhancing Table-Based Question Answering with Knowledge Graphs and Tree-Structured Reasoning by Jianbin Zhao, Pengfei Zhang, Yuzhen Wang, Rui Xin, Xiuyuan Lu, Ripeng Li, Shuai Lyu, Zhonghong Ou, Meina Song

“…We introduce TQAgent, a framework designed to enhance table-based reasoning by incorporating knowledge graphs and tree-structured reasoning paths. TQAgent reduces hallucinations and improves model reliability by grounding reasoning in external knowledge and dynamically sampling high-confidence paths. …”
Get full text

Autonomous air combat decision making via graph neural networks and reinforcement learning by Lin Huo, Chudi Wang, Yue Han

“…By leveraging the graph structure to adapt to the high dynamics and high-dimensional characteristics of multi-agent systems, the proposed approach enables rapid decision-making for missile launches through an efficient sampling strategy while employing zero-order optimization to explore global optima effectively. …”
Get full text

OWNC: Open-World Node Classification on Graphs with a Dual-Embedding Interaction Framework by Yuli Chen, Chun Wang

“…Traditional node classification is typically conducted in a closed-world setting, where all labels are known during training, enabling graph neural network methods to achieve high performance. …”
Get full text

Graph neural network-based water contamination detection from community housing information by Raphael Anaadumba, Yigit Bozkurt, Connor Sullivan, Madhavi Pagare, Pradeep Kurup, Benyuan Liu, Mohammad Arif Ul Alam

“…Traditional methods recommended by the Environmental Protection Agency (EPA) rely on collecting water samples and conducting lab tests, which can be both time-consuming and costly.Methods: To address these limitations, this study introduces a Graph Attention Network (GAT) to predict lead contamination in drinking water. …”
Get full text

Drug-drug interaction prediction of traditional Chinese medicine based on graph attention networks by Bin Yang, Dan Song, Yadong Li, Jinglong Wang

“…To address this challenge, we propose a novel Dual Graph Attention Network (DGAT) designed to predict TCM drug-drug interactions (TCMDDI) by extracting key structural features of active molecules within the herbal ingredients. …”
Get full text

Summary Graphs Covary with Reading and Language Comprehension in School-age Children in the Spanish Language by Anabel Fernández-Blanco, Nancy Estévez-Pérez, Klency González Hernández

“…Additional experimental studies in larger samples are required. …”
Get full text

Graph reinforcement learning-driven source-load cooperative scheduling optimization for textile production by Tianhao Tan, Tao Wu, Jie Li, Yuyuan Lan, Jinsong Bao

“…To capture the complex, long-range dependencies in production processes, heterogeneous attention graph neural networks are used to capture the long-range dependencies in production links, combined with an adaptive greedy sampling strategy for optimizing scheduling decisions. …”
Get full text

Joint Extraction of Hazard Source Knowledge in Integrated Utility Corridor Based on Knowledge Graph by Shanshan Wan, Houchen Lv, Yuhan Zhu, Yiran Zhao

“…The core steps in building a knowledge graph involve entity recognition and relationship extraction. …”
Get full text

Cloud Computing Resource Scheduling Algorithm Based on Unsampled Collaborative Knowledge Graph Network by Haichuan Sun, Liang Gu, Chenni Dong, Xin Ma, Zeyu Liu, Zhenxi Li

“…A cloud computing resource scheduling algorithm based on sampled collaborative knowledge graph network is designed to address the issues of lag in the process of cloud computing resource scheduling, high overall load rate, and large transient amplitude and phase errors. …”
Get full text

Spatial Multifeature and Dual-Layer Multihop Graph Convolution Networks for Hyperspectral Image Classification by Xiangyue Yu, Ning Li, Di Wu, Zheng Li, Zhenyuan Wu, Ximing Ma

“…Convolutional neural networks (CNNs) and graph convolutional network (GCN) have exhibited outstanding classification performance in this field, emerging as current research focuses. …”
Get full text

Spatial-Spectral Contrastive Graph Neural Network for Few-Shot Hyperspectral Image Classification by Zhaoxia Xue, Zhiwei Liu, Zhaohui Xue, Tingqiang Song

“…Extensive experiments on four benchmark HSI datasets—University of Pavia (PU), Salinas Valley (SA), WHU-Hi-HanChuan (WHU-HH), and WHU-Hi-LongKou (WHU-HL)—demonstrate SSCGNN’s superiority over state-of-the-art few-shot learning methods. With only 5 labeled samples per class, our model achieves overall accuracy (OA) values of: 93.36% (PU, +6.22% vs. baseline GCN), 98.37% (SA, +4.91% vs. spectral-spatial CNN), 84.68% (WHU-HH, +8.03% vs. meta-learning approaches), 95.87% (WHU-HL, +7.15% vs. graph attention networks).…”
Get full text

Wind Power Forecasting Based on Multi-Graph Neural Networks Considering External Disturbances by Xiaoyin Xu, Zhumei Luo, Menglong Feng

“…The framework adopts a three-component architecture consisting of (1) a multi-graph convolutional network using both geographical proximity and power correlation graphs to capture heterogeneous spatial dependencies between wind farms, (2) an attention-enhanced LSTM network that weights temporal features differentially based on their predictive significance, and (3) a specialized Conv2D mechanism to identify and isolate external disturbance patterns. …”
Get full text