VAE-Assisted Data Augmentation for Improved Molecular Prediction with Graph Neural Networks (GNNs) in Low-Data Regimes by Gabriela C. Theis Marchan, Pegah Naghshnejad, Andrew Okafor, Jose A. Romagnoli

“…This study presents a novel approach to enhancing molecular property prediction through variational autoencoder (VAE)-assisted data augmentation in low-data regimes. The methodology combines graph neural networks (GNNs) with VAEs to improve predictive accuracy on molecular datasets from MoleculeNet, specifically ESOL (water solubility) and FreeSolv (hydration-free energy). …”
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A model of feature extraction for well logging data based on graph regularized non-negative matrix factorization with optimal estimation by Kehong Yuan, Youlin Shang, Haixiang Guo, Yongsheng Dong, Zhonghua Liu

“…Firstly, the low dimensional embedding dimension of high-dimensional well logging data is modeled and estimated, which enables the method to obtain the appropriate number of features that reflect the data structure. …”
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Efficient knowledge graph to text powered by LLGM: linear latent graph model by Xiaokang Zhao, Yao Zheng, Yubo Shan, Jingyuan Li, Kun Zhang, Yuanzhuo Wang

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Incorporating Topological Priors Into Low-Dimensional Visualizations Through Topological Regularization by Edith Heiter, Robin Vandaele, Tijl de Bie, Yvan Saeys, Jefrey Lijffijt

“…Unsupervised representation learning techniques are commonly employed to analyze high-dimensional or unstructured data. In some cases, users may have prior knowledge of the topology of the data, such as a known cluster structure or the fact that it follows a tree- or graph-based structure. …”
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CancerClarity app: Enhancing cancer data visualization with AI-generated narratives by Edgar Munoz, Alexander D. VanHelene, Nuen Tsang Yang, Amelie G. Ramirez

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Adversarial Graph Regularized Deep Nonnegative Matrix Factorization for Data Representation by Songtao Li, Weigang Li, Yang Li

“…Specifically, for the purpose of effectively learning low-dimensional representations of data. This work constructs a deep NMF method to approximate the coding structure of the original data, which enables AGDNMF to learn the hierarchical mapping relationship between the categories of the original data, and learn the hidden association information of the data from low-to-high in the middle layer. …”
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Automatic Generation Method of ZC Line Data Based on Directed Graph by LI Pengdong, TAN Litian, LEI Dading, CHEN Xin

“…The current CBTC(communication based train control) signaling system has the problems that its ZC(zone controller) has a large amount of line data to be manually entered and the line data storage and access is of low efficiency. …”
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Low-Observability Distribution System State Estimation by Graph Computing with Enhanced Numerical Stability by Zijian Hu, Hong Zhu, Lan Lan, Honghua Xu, Zichen Liu, Kexin Li, Jie Li, Zhinong Wei

“…In distribution systems, limited measurement configurations and communication constraints often result in a low success rate of data acquisition, posing challenges to both system observability and the real-time performance required by state estimation (SE). …”
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Evaluating Graph Attention Networks as an Alternative to Transformers for ABSA Task in Low-Resource Languages by Gabriel Gomes, Alexandre T. Bender, Arthur Cerveira, Larissa A. Freitas, Ulisses B. Corrˆea

“…While studies have demonstrated the effectiveness of this representation for Aspect-based Sentiment Analysis using Graph Neural Networks in English, there is only sparse evidence of improvement using these techniques for low-resource languages such as Portuguese. …”
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Molecular property prediction in the ultra‐low data regime by Basem A. Eraqi, Dmitrii Khizbullin, Shashank S. Nagaraja, S. Mani Sarathy

“…By enabling reliable property prediction in low-data regimes, ACS broadens the scope and accelerates the pace of artificial intelligence-driven materials discovery and design.…”
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Low‐rank isomap algorithm by Eysan Mehrbani, Mohammad Hossein Kahaei

“…Its computational complexity mainly arises from two stages; a) embedding a full graph on the data in the ambient space, and b) a complete eigenvalue decomposition. …”
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Integration of unpaired single cell omics data by deep transfer graph convolutional network. by Yulong Kan, Yunjing Qi, Zhongxiao Zhang, Xikeng Liang, Weihao Wang, Shuilin Jin

“…Here, we present a robust deep transfer model based graph convolutional network, scTGCN, which achieves versatile performance in preserving biological variation, while achieving integration hundreds of thousands cells in minutes with low memory consumption. …”
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Alternative audio-graphic method for presenting structural information in mathematical graphs designed for low-vision users by Ewa Dzierzgowska, Michał Maćkowski, Mateusz Kawulok, Piotr Brzoza, Stella Maćkowska, Dominik Spinczyk

“…Abstract Despite advances in assistive technologies, existing tools for teaching mathematics to students with low vision often fail to effectively convey structural information in graphs and function plots. …”
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RETRACTED: Temporal-Difference Graph-Based Optimization for High-Quality Reconstruction of MODIS NDVI Data by Shengtai Ji, Shuxin Han, Jiaxin Hu, Yuguang Li, Jing-Cheng Han

“…In this study, we proposed a novel approach for employing a Temporal-Difference Graph (TDG) method to reconstruct low-quality pixels in NDVI data. …”
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Unlabeled-Data-Enhanced Tool Remaining Useful Life Prediction Based on Graph Neural Network by Dingli Guo, Honggen Zhou, Li Sun, Guochao Li

“…Since multiple sensors are frequently used to simultaneously collect cutting data, this paper uses a graph neural network (GNN) for multi-sensor data fusion, extracting more useful information from the data to improve unlabeled data enhancement. …”
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Robust Low-Snapshot DOA Estimation for Sparse Arrays via a Hybrid Convolutional Graph Neural Network by Hongliang Zhu, Hongxi Zhao, Chunshan Bao, Yiran Shi, Wenchao He

“…This approach capitalizes on the increased degrees of freedom offered by the virtual array while inherently incorporating the array’s geometric relationships via graph-based learning. The proposed C-GNN demonstrates robust performance in noisy, low-data scenarios, reliably estimating source angles even with very limited snapshots. …”
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A Topology Identification Strategy of Low-Voltage Distribution Grids Based on Feature-Enhanced Graph Attention Network by Yang Lei, Fan Yang, Yanjun Feng, Wei Hu, Yinzhang Cheng

“…Then, the F-GAT model is used to learn potential association patterns and structural information in the graph topology, achieving a joint low-dimensional representation of electrical attributes and graph semantics. …”
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Semantic Fusion of Health Data: Implementing a Federated Virtualized Knowledge Graph Framework Leveraging Ontop System by Abid Ali Fareedi, Stephane Gagnon, Ahmad Ghazawneh, Raul Valverde

“…Using a virtualized technique, the FVKG helps to reduce data migration, ensures low latency and dynamic freshness, and facilitates real-time access while upholding integrity and coherence throughout the federation system. …”
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Application of Graph-Theoretic Methods Using ERP Components and Wavelet Coherence on Emotional and Cognitive EEG Data by Sencer Melih Deniz, Ahmet Ademoglu, Adil Deniz Duru, Tamer Demiralp

“…In this study, we discriminated pleasant/unpleasant emotional moods and low/high cognitive states using graph-theoretic features extracted from spatio-temporal components. …”
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Research on big data technology for military human resources combining knowledge graph and large language model by FENG Qi, WANG Jigang, WANG Jian

“…This technology eliminates data silos by constructing multi-source knowledge graph, and introduces large language model to improve intelligent interaction ability, so as to solve the problems of difficult data application and low degree intelligent interaction in the field of military human resources. …”
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