GAT-ADNet: Leveraging Graph Attention Network for Optimal Power Flow in Active Distribution Network With High Renewables by Dinesh Kumar Mahto, Mahipal Bukya, Rajesh Kumar, Akhilesh Mathur, Vikash Kumar Saini

“…This paper proposes a high-fidelity graph attention networks (GAT) model that leverages the attention mechanism and graph convolution feature mapping property to learn neighbor informative node representations for OPF solutions. …”
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Realizing string-net condensation: Fibonacci anyon braiding for universal gates and sampling chromatic polynomials by Zlatko K. Minev, Khadijeh Najafi, Swarnadeep Majumder, Juven Wang, Ady Stern, Eun-Ah Kim, Chao-Ming Jian, Guanyu Zhu

“…Coupling the DSNP approach with composite error-mitigation on deep circuits, we create, measure, and braids Fibonacci anyons; charge measurements show 94% accuracy, and exchanging the anyons yields the expected golden ratio ϕ with 98% average accuracy. We then sample the Fib SNC to estimate chromatic polynomial at ϕ + 2 for several graphs. …”
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DAGSLAM: causal Bayesian network structure learning of mixed type data and its application in identifying disease risk factors by Yuanyuan Zhao, Jinzhu Jia

“…Results Extensive simulations were conducted across eight distinct scenarios, specifically, variations in the number of nodes, changes in the proportion of categorical nodes, different sample sizes, levels of categorical nodes, variations in edge sparsity, adjustments to the weight scale, different graph types, and diverse noise distributions. …”
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Elaboration of a Method for the Determination of Low-Molecular-Weight Mustard Gas Biomarkers in Biological Samples by D. О. Korneev, L. V. Petrakova, М. А. Ponsov, А. А. Rodionov

“…After the chromatographic analysis, the corresponding graphs of indicators have been constructed, based on the concentrations of biomarkers in urine and blood plasma. …”
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A Novel Biclustering Algorithm Based on Differential Sparsity Constraints and Dynamic Graph Regularization for Cancer Gene Expression Data by Dan Li, Peicong Song, Jia Wang

“…To address these issues, this paper proposes a novel biclustering algorithm based on differential sparsity constraints and dynamic graph regularization (BCDD). On one hand, considering that the cancer gene expression data contains numerous redundant genes unrelated to the disease, while all samples belong to a specific cancer subtype or come from healthy subjects, the proposed algorithm imposes <inline-formula> <tex-math notation="LaTeX">$l_{\mathrm {1/2}}$ </tex-math></inline-formula>-norm and <inline-formula> <tex-math notation="LaTeX">$l_{1}$ </tex-math></inline-formula>-norm constraints on gene and sample dimensions, respectively, to better capture the differences in sparsity between these two dimensions. …”
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A Construction and Representation Learning Method for a Traffic Accident Knowledge Graph Based on the Enhanced TransD Model by Xiaojia Liu, Haopeng Wu, Dexin Yu, Yunjie Chen, Hao Wu

“…In this study, following a top-down ontology design principle, we construct a California Traffic Accident Knowledge Graph (TAKG) encompassing over one hundred elements, and propose an enhanced TransD embedding model. …”
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stClinic dissects clinically relevant niches by integrating spatial multi-slice multi-omics data in dynamic graphs by Chunman Zuo, Junjie Xia, Yupeng Xu, Ying Xu, Pingting Gao, Jing Zhang, Yan Wang, Luonan Chen

“…However, challenges like data scale and diversity, disease heterogeneity, and limited sample population size, impede the derivation of clinical insights. …”
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Boosting Deep Reinforcement Learning with Semantic Knowledge for Robotic Manipulators by Lucía Güitta-López, Vincenzo Suriani, Jaime Boal, Álvaro J. López-López, Daniele Nardi

“…These results highlight the potential of semantic knowledge to reduce sample complexity and improve the effectiveness of DRL in robotic applications.…”
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Leveraging commonality across multiple tissue slices for enhanced whole slide image classification using graph convolutional networks by Sakonporn Noree, Willmer Rafell Quinones Robles, Young Sin Ko, Mun Yong Yi

“…Our method constructs graphs for each tissue slice, extracts relevant features, and connects these graphs based on spatial relationships and feature similarities, creating a comprehensive representation of the entire tissue sample, which is then used for WSI classification using graph convolutional networks. …”
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Dispersive Liquid-Liquid Microextraction of Bismuth in Various Samples and Determination by Flame Atomic Absorption Spectrometry by Teslima Daşbaşı, Şenol Kartal, Şerife Saçmacı, Ahmet Ülgen

“…A dispersive liquid-liquid microextraction method for the determination of bismuth in various samples by flame atomic absorption spectrometry is described. …”
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ACGRHA-Net: Accelerated multi-contrast MR imaging with adjacency complementary graph assisted residual hybrid attention network by Haotian Zhang, Qiaoyu Ma, Yiran Qiu, Zongying Lai

“…To address this, we propose a method that reconstructs multi-contrast MR images from zero-filled data by utilizing a fully-sampled auxiliary contrast MR image as a prior to learn an adjacency complementary graph. …”
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Structured Analysis of a Generator Set Lubrication System Using Vertex Articulation in Plithogenic n-SuperHyperGraphs by Gonzalo X. Vizuete, Cristian F. Gallardo, Gabriela Vizuete

“…This study assessed particulate contamination by applying the ISO 4406 cleanliness code, evaluating oil properties, and monitoring engine wear, using the Plithogenic n-SuperHyperGraph model as a structural framework. Oil samples were analyzed before and after implementing an external microfiltration system. …”
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ANALYSIS OF A DECADE OF STUDENT GRADES IN THE ELECTRICAL ENGINEERING DEPARTMENT AT SHARIF UNIVERSITY OF TECHNOLOGY USING GRAPH SIGNAL PROCESSING by Amirhossein Golshirazi, Reza Parhizkar, Arash Amini, Mohammad Mahdi Omati

“…Importantly, all data was utilized, and no sampling was applied. Each student is represented as a node in a graph, and the nodes are connected through weighted edges based on the similarity of academic performance. …”
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A Scene Graph Generation Method for Historical District Street-view Imagery: A Case Study in Beijing, China by X. Guo, X. Liu, J. Jiang

“…By incorporating an end-to-end Relation Transformer with the parameter-free attention and coordinate attention modules, HSSGG improves relationship prediction accuracy, even with limited samples, and enhances the precision of scene graph generation in complex environments. …”
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A diagnosis method based on graph neural networks embedded with multirelationships of intrinsic mode functions for multiple mechanical faults by Bin Wang, Manyi Wang, Yadong Xu, Liangkuan Wang, Shiyu Chen, Xuanshi Chen

“…Additionally, a graph-level based fault diagnosis network model is designed to enhance feature learning capabilities for graph samples and enable flexible application across diverse signal sources and devices. …”
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Graph-Based Few-Shot Learning for Synthetic Aperture Radar Automatic Target Recognition with Alternating Direction Method of Multipliers by Jing Jin, Zitai Xu, Nairong Zheng, Feng Wang

“…To address this challenge, we propose a novel few-shot learning (FSL) framework: the alternating direction method of multipliers–graph convolutional network (ADMM-GCN) framework. …”
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Heterogeneous network drug-target interaction prediction model based on graph wavelet transform and multi-level contrastive learning by Wenfeng Dai, Yanhong Wang, Shuai Yan, Qingzhi Yu, Xiang Cheng

“…Second, heterogeneous data fusion integrates molecular graphs, protein structure graphs, and bioactivity data via cross-graph attention, enabling interpretable residue-level insights. …”
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Urban street network morphology classification through street-block based graph neural networks and multi-model fusion by Yang Liu, Qingsheng Guo, Chuanbang Zheng

“…SBGNet represents urban street networks as graphs, with nodes as street blocks and geometric features as node attributes, while spatial arrangements serve as edge features. …”
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Intelligent data-driven system for mold manufacturing using reinforcement learning and knowledge graph personalized optimization for customized production by Chengcai He, Jiaxing Deng, Jingchun Wu, Beicheng Qin, Jinxiang Chen, Yan Li, Qiangsheng Huang

“…The experimental results reveal two key findings: (1) Within the enhanced learning knowledge graph framework, the algorithm—optimized using a graph convolutional network—achieves consistently higher qualification rates across test samples. …”
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Multi-atlas ensemble graph neural network model for major depressive disorder detection using functional MRI data by Nojod M. Alotaibi, Areej M. Alhothali, Manar S. Ali

“…Recent years have seen a rise in interest in graph neural networks (GNNs), which are deep neural architectures specifically designed to handle graph-structured data like rs-fMRI. …”
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