Using Proximity Graph Cut for Fast and Robust Instance-Based Classification in Large Datasets by Stanislav Protasov, Adil Mehmood Khan

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Discriminatively Constrained Semi-Supervised Multi-View Nonnegative Matrix Factorization with Graph Regularization by Guosheng Cui, Ye Li, Jianzhong Li, Jianping Fan

“…Nonnegative Matrix Factorization (NMF) is one of the most popular feature learning technologies in the field of machine learning and pattern recognition. …”
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Graph-Based Prediction of Spatio-Temporal Vaccine Hesitancy From Insurance Claims Data by Sifat Afroj Moon, Rituparna Datta, Tanvir Ferdousi, Hannah Baek, Abhijin Adiga, Achla Marathe, Anil Vullikanti

“…We find that an aggregated contact network or graph, developed from a detailed activity-based population network, plays an important role in the performance of VaxHesSTL, compared to graph models based solely on spatial proximity. …”
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GNODEVAE: a graph-based ODE-VAE enhances clustering for single-cell data by Zeyu Fu, Chunlin Chen, Song Wang, Junping Wang, Shilei Chen

“…Extensive comparison across 50 diverse single cell datasets against 18 existing methods demonstrates that GNODEVAE consistently outperforms three major categories of benchmark methods: 8 machine learning dimensionality reduction techniques, 7 deep generative VAE variants, and 3 graph-based and contrastive learning deep predictive models. …”
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Towards data-driven electricity management: multi-region uniform data and knowledge graph by Vid Hanžel, Blaž Bertalanič, Carolina Fortuna

“…This paper introduces a multi-region dataset compiled from publicly available sources and presented in a uniform format. This data enables machine learning tasks such as disaggregation, demand forecasting, appliance ON/OFF classification, etc. …”
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Multi‐Distance Spatial‐Temporal Graph Neural Network for Anomaly Detection in Blockchain Transactions by Shiyang Chen, Yang Liu, Qun Zhang, Zhouhang Shao, Zewei Wang

“…This article presents MDST‐GNN, a multi‐distance spatial‐temporal graph neural network for blockchain anomaly detection. …”
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SL-GCNN: A Graph Convolutional Neural Network for Granular Human Motion Recognition by Yang Li, Jingyu Zhang

“…Human motion recognition has significantly advanced, with applications in human-computer interaction, virtual reality, intelligent video surveillance, and athletic training. This paper presents SL-GCNN, a novel Graph Convolutional Neural Network framework specifically designed for granular skeletal motion recognition. …”
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PLGNN: graph neural networks via adaptive feature perturbation and high-way links by Meixia He, Peican Zhu, Yang Liu, Keke Tang

“…Abstract Graph neural networks (GNNs) have exhibited remarkable performance in addressing diverse graph learning tasks. …”
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A Large Language Model Driven Knowledge Graph Construction Scheme for Semantic Communication by Chang Guo, Jiaqi Liu, Wei Gao, Zhenhai Lu, Yao Li, Chengyuan Wang, Jungang Yang

“…This study presents a knowledge graph construction scheme leveraging large language models (LLMs) for task-oriented semantic communication systems. …”
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Observation of a rare beta decay of the charmed baryon with a Graph Neural Network by The BESIII Collaboration

“…A novel Graph Neural Network based technique effectively separates signals from dominant backgrounds, notably $${\Lambda }_{c}^{+}\to \Lambda {e}^{+}{\nu }_{e}$$ Λ c + → Λ e + ν e , achieving a statistical significance exceeding 10σ. …”
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Enhanced Radar Signal Classification Using AMP and Visibility Graph for Multi-Signal Environments by Ji-Hyeon Kim, Soon-Young Kwon, Hyoung-Nam Kim

“…Accurately classifying and deinterleaving overlapping radar signals presents a significant challenge in complex environments, such as electronic warfare. …”
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Enhancing biometric identification using 12-lead ECG signals and graph convolutional networks by Maram Al Alfi, Pedro Peris-Lopez, Carmen Camara

“…IntroductionThe electrocardiogram (ECG) is a highly secure biometric modality due to its intrinsic physiological characteristics, making it resilient to forgery and external attacks. This study presents a novel real-time biometric authentication system integrating Graph Convolutional Networks (GCN) with Mutual Information (MI) indices extracted from 12-lead ECG signals.MethodsThe MI index quantifies the statistical dependencies among ECG leads and is computed using entropy-based estimations. …”
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GraphVelo allows for accurate inference of multimodal velocities and molecular mechanisms for single cells by Yuhao Chen, Yan Zhang, Jiaqi Gan, Ke Ni, Ming Chen, Ivet Bahar, Jianhua Xing

“…Yet, several inherent limitations restrict applying the approaches to genes not suitable for RNA velocity inference due to complex transcriptional dynamics, low expression, or lacking splicing dynamics, or data of non-transcriptomic modality. Here, we present GraphVelo, a graph-based machine learning procedure that uses as input the RNA velocities inferred from existing methods and infers velocity vectors lying in the tangent space of the low-dimensional manifold formed by the single cell data. …”
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Didactics of the Fundamentals of Computer Programming with a Computational Thinking Approach Based on Turtle Graphs by Jesus Insuasti, Edwin Insuasty, Alexander Baron

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

“…Experimental results reveal that the proposed DGAT method significantly outperforms currently advanced deep learning techniques, including Graph Convolutional Networks, Weave, and Message Passing Neural Networks. …”
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Graph-based adaptive feature fusion neural network model for person-job fit by Xia Xue, Feilong Wang, Jingwen Wang, Bo Ma, Yuyang Yu, Shuling Gao, Jing Chen, Baoli Wang

“…Previous studies on person-job fit fail to explore job seekers’ resume information from a multi-perspective approach, and neglect the sustainable learning of resume features. To address this, the present paper proposes a Graph-based Person-Job Fit Neural Network Fusion (GPJFNNF) model. …”
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Copy-Move Forgery Detection Technique Using Graph Convolutional Networks Feature Extraction by Varun Shinde, Vineet Dhanawat, Ahmad Almogren, Anjanava Biswas, Muhammad Bilal, Rizwan Ali Naqvi, Ateeq Ur Rehman

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Multi-scale spatio-temporal graph neural network for urban traffic flow prediction by Hui Chen, Jian Huang, Yong Lu, Jijie Huang

“…In response to the above challenges, this paper proposes a novel Spatio-Temporal Graph neural network with Multi-timeScale (abbreviated as STGMS). …”
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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

“…To resolve these challenges, this paper presents a graph computing-based DSSE method with enhanced numerical stability for low-observability distribution systems. …”
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Multi-view mixture-of-experts for predicting molecular properties using SMILES, SELFIES, and graph-based representations by Eduardo Soares, Victor Yukio Shirasuna, Emilio Vital Brazil, Indra Priyadarsini, Seiji Takeda

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