Multiradar Collaborative Task Scheduling Algorithm Based on Graph Neural Networks with Model Knowledge Embedding by Haoqing LI, Dian YU, Changchun PAN, Wenxian YU, Dongying LI

“…The diversity of task types, limited data resources, and strict execution time requirements make radar task scheduling a strongly NP-hard problem. …”
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GNNs and ensemble models enhance the prediction of new sRNA-mRNA interactions in unseen conditions by Shani Cohen, Lior Rokach, Isana Veksler-Lublinsky

“…The SEM model combining GraphRNA and CopraRNA outperformed CopraRNA alone on a low-throughput (LT) interactions test set (HT-to-LT evaluation). …”
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Low-Complexity Gaussian Detection for MIMO Systems by Tianbin Wo, Peter Adam Hoeher

“…Using factor graphs as a general framework and applying the Gaussian approximation, three low-complexity iterative detection algorithms are derived, and their performances are compared by means of Monte Carlo simulations. …”
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Firmware Attestation in IoT Swarms Using Relational Graph Neural Networks and Static Random Access Memory by Abdelkabir Rouagubi, Chaymae El Youssofi, Khalid Chougdali

“…Unlike conventional Graph Neural Networks (GNNs), RGNNs incorporate edge types (e.g., Prompt, Sensor Data, Processed Signal), enabling finer-grained detection of propagation dynamics. …”
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Enhancing the Distinguishability of Minor Fluctuations in Time Series Classification Using Graph Representation: The MFSI-TSC Framework by He Nai, Chunlei Zhang, Xianjun Hu

“…In industrial systems, sensors often classify collected time series data for incipient fault diagnosis. However, time series data from sensors during the initial stages of a fault often exhibits minor fluctuation characteristics. …”
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A lightweight knowledge graph-driven question answering system for field-based mineral resource survey by Mingguo Wang, Chengbin Wang, Jianguo Chen, Bo Wang, Wei Wang, Xiaogang Ma, Jiangtao Ren, Zichen Li, Yicai Ye, Jiakai Zhang, Yue Wang

“…Initially, we utilized deep-learning-based geological entities and their semantic relation recognition, along with relational data mapping, to construct the mineral resource survey knowledge graph based on the ontology model. …”
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L2-GNN: Graph neural networks with fast spectral filters using twice linear parameterization by Siying Huang, Xin Yang, Zhengda Lu, Hongxing Qin, Huaiwen Zhang, Yiqun Wang

“…The parameterization allows for an enlarged receptive field of graph convolutions, which can simultaneously capture low-frequency and high-frequency information. …”
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PEGSGraph: A Graph Neural Network for Fast Earthquake Characterization Based on Prompt ElastoGravity Signals by Céline Hourcade, Kévin Juhel, Quentin Bletery

“…Prompt elastogravity signals (PEGS) are low‐amplitude, light‐speed signals emitted by earthquakes, which are highly sensitive to both their magnitude and focal mechanism. …”
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The Air Transportation System as a Subsystem of Modern Communication Space: Analysis Based on Transfer Entropy Graphs by Sagit Valeev, Natalya Kondratyeva

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A knowledge graph attention network for the cold‐start problem in intelligent manufacturing: Interpretability and accuracy improvement by Ziye Zhou, Yuqi Zhang, Shuize Wang, David San Martin, Yongqian Liu, Yang Liu, Chenchong Wang, Wei Xu

“…Abstract In the rolling production of steel, predicting the performance of new products is challenging due to the low variety of data distributions resulting from standardized manufacturing processes and fixed product categories. …”
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Med-DGTN: Dynamic Graph Transformer with Adaptive Wavelet Fusion for multi-label medical image classification by Guanyu Zhang, Yan Li, Tingting Wang, Guokun Shi, Li Jin, Zongyun Gu, Zongyun Gu

“…To address these challenges, we propose Med-DGTN, a dynamically integrated framework designed to advance multi-label classification performance in clinical imaging analytics.MethodsThe proposed Med-DGTN (Dynamic Graph Transformer Network with Adaptive Wavelet Fusion) introduces three key innovations: (1) A cross-modal alignment mechanism integrating convolutional visual patterns with graph-based semantic dependencies through conditionally reweighted adjacency matrices; (2) Wavelet-transform-enhanced dense blocks (WTDense) employing multi-frequency decomposition to amplify low-frequency pathological biomarkers; (3) An adaptive fusion architecture optimizing multi-scale feature hierarchies across spatial and spectral domains.ResultsValidated on two public medical imaging benchmarks, Med-DGTN demonstrates superior performance across modalities: (1) Achieving a mean average precision (mAP) of 70.65% on the retinal imaging dataset (MuReD2022), surpassing previous state-of-the-art methods by 2.68 percentage points. (2) On the chest X-ray dataset (ChestXray14), Med-DGTN achieves an average Area Under the Curve (AUC) of 0.841. …”
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User-cooperative dynamic resource allocation for backscatter-aided wireless-powered MEC network by Huaiwen He, Chenghao Zhou, Feng Huang, Hong Shen, Yihong Yang, Shangsong Liang, Xinyuan Jin

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Descriptive Data Analysis of Tuberculosis Surveillance Data, Sene East District, Ghana, 2020 by Rapheal Gyan Asamoah, Delia Akosua Bandoh, Samuel Dapaa, Joseph Asamoah Frimpong, Ernest Kenu

“…Data was presented in graphs and tables using Microsoft Excel 2016. …”
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DeepWalk-Based Graph Embeddings for miRNA–Disease Association Prediction Using Deep Neural Network by Jihwan Ha

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SGRiT: Non-Negative Matrix Factorization via Subspace Graph Regularization and Riemannian-Based Trust Region Algorithm by Mohsen Nokhodchian, Mohammad Hossein Moattar, Mehrdad Jalali

“…The method leverages a spectral decomposition criterion to obtain a low-dimensional embedding that captures the intrinsic geometric structure of the data. …”
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SDDGRNets: Level–Level Semantically Decomposed Dynamic Graph Reasoning Network for Remote Sensing Semantic Change Detection by Zhuli Xie, Gang Wan, Yunxia Yin, Guangde Sun, Dongdong Bu

“…This network aims to understand and perceive subtle changes in the semantic content of remote sensing data from the image pixel level. On the one hand, low-level semantic information and cross-scale spatial local feature details are obtained by dividing subspaces and decomposing convolutional layers with significant kernel expansion. …”
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HGATLink: single-cell gene regulatory network inference via the fusion of heterogeneous graph attention networks and transformer by Yao Sun, Jing Gao

“…However, the sparsity and noise problems of single-cell sequencing data pose challenges for gene regulatory network inference, and although many gene regulatory network inference methods have been proposed, they often fail to eliminate transitive interactions or do not address multilevel relationships and nonlinear features in the graph data well. …”
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A multi-view graph neural network approach for magnetic resonance imaging-based diagnosis of knee injuries by Biyong Deng, Jiashan Pan, Xiaoyu Tang, Haitao Fu, Shushan Hu

“…Although several computational methods have been proposed to diagnose knee injuries, most rely heavily on local features in MRI images and exhibit low prediction accuracy. In this paper, we proposed a novel multi-view graph neural network, abbreviated as MVGNN, to identify knee injuries (specifically ACL tears and meniscal tears) by leveraging graph representations derived from multiple MRI views. …”
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Neurocognitive Biotypes of Risk and Resilience for Mood Disorders in Adolescents: Insights From Behavioral and Graph-Theoretic Network Markers by Ambra Coccaro, Ziwei Cheng, Luka Ruzic, Amelia D. Moser, Jenna Jones, Elena C. Peterson, Elisa F. Stern, Naomi P. Friedman, Roselinde H. Kaiser

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stGuide advances label transfer in spatial transcriptomics through attention-based supervised graph representation learning by Yupeng Xu, Hao Dai, Jinwang Feng, Keren Xu, Qiu Wang, Pingting Gao, Chunman Zuo, Chunman Zuo

“…The growing availability of spatial transcriptomics data offers key resources for annotating query datasets using reference datasets. …”
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