G-KAN: Graph Kolmogorov-Arnold Network for Node Classification Using Contrastive Learning by Lining Yuan

“…Graph Convolutional Networks (GCN) and their variants utilize learnable weight matrices and nonlinear activation functions to extract features from data. …”
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A Graph Convolutional Network Framework for Area Attention and Tracking Compensation of In-Orbit Satellite by Shuai Wang, Ruoke Wu, Yizhi Jiang, Xiaoqiang Di, Yining Mu, Guanyu Wen, Makram Ibrahim, Jinqing Li

“…In order to solve the problems of low tracking accuracy of in-orbit satellites by ground stations and slow processing speed of satellite target tracking images, this paper proposes an orbital satellite regional tracking and prediction model based on graph convolutional networks (GCNs). …”
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Graph Neural Network-Based Attribute Auxiliary Structured Grouping for Person Re-Identification by Geyu Tang, Xingyu Gao, Zhenyu Chen, Huicai Zhong

“…Although state-of-the-art clustering-based methods have achieved good performance, the pseudo labels generated through clustering are often low-quality and noisy. To address this problem, we propose a graph neural network based Attribute Auxiliary structured Grouping (<inline-formula> <tex-math notation="LaTeX">$ \text {A}^{2}$ </tex-math></inline-formula>G) to improve the confidence of the pseudo labels. …”
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Graph reinforcement learning-driven source-load cooperative scheduling optimization for textile production by Tianhao Tan, Tao Wu, Jie Li, Yuyuan Lan, Jinsong Bao

“…This research proposes a graph reinforcement learning-driven source-load collaborative optimization method. …”
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Enhanced Spatiotemporal Landslide Displacement Prediction Using Dynamic Graph-Optimized GNSS Monitoring by Jiangfeng Li, Jiahao Qin, Kaimin Kang, Mingzhi Liang, Kunpeng Liu, Xiaohua Ding

“…The framework’s core innovation lies in a dynamic graph optimization model with low-rank constraints, which adaptively refines the graph topology to reflect time-varying inter-sensor dependencies driven by factors like mining activities. …”
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An Efficient Topology Construction Scheme Designed for Graph Neural Networks in Hyperspectral Image Classification by Yu Zhang, Xin Li, Yaoqun Xu

“…Superpixel-based Graph Neural Networks (GNNs) have achieved remarkable success in hyperspectral image (HSI) classification tasks, primarily due to their ability to capture the implicit topological structure in the data while maintaining low computational complexity by propagating information between spatially adjacent superpixels. …”
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Hybrid Graph Representation and Learning Framework for High-Level Synthesis Design Space Exploration by Pouya Taghipour, Eric Granger, Yves Blaquiere

“…Learning-based methods, particularly graph neural networks (GNNs), have shown considerable potential in addressing HLS QoR/DSE problems by modeling the mapping function from control data flow graphs (CDFGs) of HLS designs to their logic, enabling early estimation of QoR during the compilation phase of the hardware design flow. …”
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EKNet: Graph Structure Feature Extraction and Registration for Collaborative 3D Reconstruction in Architectural Scenes by Changyu Qian, Hanqiang Deng, Xiangrong Ni, Dong Wang, Bangqi Wei, Hao Chen, Jian Huang

“…The vertices and edges of geometric structure models are sparse, and existing methods face challenges such as low feature extraction efficiency and substantial data requirements when processing sparse graph structures after geometrization. …”
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Solana’s transaction network: analysis, insights, and comparison by Sajjad Alizadeh, Majid Khabbazian

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Toward Robust GNSS Real-Time Orbit Determination for Microsatellites Using Factor Graph Optimization by Cong Hou, Xiaojun Jin, Xiaopeng Yang, Tong Xiao

“…Furthermore, the performance of FGO-RTOD is assessed in challenging scenarios using simulation data and on-orbit data from Tianping-2B microsatellite, which is not in an Earth-pointing attitude and employs a low-cost receiver. …”
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Bearing Faults Diagnosis Method Based on Stacked Auto-Encoder With Graph Regularization for Wind Turbines by LIU Zhan, LIU Jianxun, BAO Yanyang, LI Dazi

“…In the fault feature extraction stage, the diagnostic signal was first graphically represented, and then graph regularization terms were added to the stacked auto-encoder to ensure that the embedded low-dimensional features maintain the manifold structure, thereby extracting complex geometric features deep in the data.ResultsThe extracted features can accurately classify different fault types, showing significant advantages in fault feature capture. …”
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LMGD: Log-Metric Combined Microservice Anomaly Detection Through Graph-Based Deep Learning by Xu Liu, Yuewen Liu, Miaomiao Wei, Peng Xu

“…However, the existing microservice anomaly detection methods do not pay attention to the multi-source data of the microservice system and thus have low accuracy. …”
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Transforming Public Services Management: A P-Graph Methodology Case Study and Scenario Analysis  by Boglárka Eisinger, László Buics

“…This study advances university enrolment optimization in Public Services Management towards sustainability, utilizing case studies, scenario analyses, and P-Graph methodology. The study evaluates administrative workloads across three intensity levels—low, average, and highly overloaded—and enhance our methodology by incorporating data that influences the process's inception and conclusion in all scenarios. …”
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Hyperledger Fabric Graph Isomorphism Network for Conflict Transactions Detection in Multi-Version Concurrency Control by Fawaz Alzahrani, Mohd Yazid Idris, Mohd Fo'Ad Rohani, Rahmat Budiarto

“…Employing advanced graph neural networks, HFGIN utilizes node and edge data representations within a graph-based framework, which significantly increases the efficiency of detecting MVCC conflicts. …”
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Ensemble Network Graph-Based Classification for Botnet Detection Using Adaptive Weighting and Feature Extraction by Muhammad Aidiel Rachman Putra, Tohari Ahmad, Dandy Pramana Hostiadi, Royyana Muslim Ijtihadie

“…Previous research has demonstrated that security systems can identify attacks by analyzing communication among bots in a network using a graphing approach. While this analytical method demonstrates satisfactory accuracy, it still faces challenges related to low recall, precision, and F1-score, due to issues such as imbalanced data and the complexity of botnet behavior. …”
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SheepDoctor: A knowledge graph enhanced large language model for sheep disease diagnosis by Jiayi Xiong, Yong Zhou, Fang Tian, Fuchuan Ni, Liang Zhao

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Lightweight pose estimation spatial-temporal enhanced graph convolutional model for miner behavior recognition by WANG Jianfang, DUAN Siyuan, PAN Hongguang, JING Ningbo

“…Graph convolutional networks (GCNs) have advantages in processing skeleton sequence data. …”
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Fault-Tolerant Scheduling Mechanism for Dynamic Edge Computing Scenarios Based on Graph Reinforcement Learning by Yuze Zhang, Geming Xia, Chaodong Yu, Hongcheng Li, Hongfeng Li

“…With the proliferation of Internet of Things (IoT) devices and edge nodes, edge computing has taken on much of the real-time data processing and low-latency response tasks which were previously managed by cloud computing. …”
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Efficient Visual-Aware Fashion Recommendation Using Compressed Node Features and Graph-Based Learning by Umar Subhan Malhi, Junfeng Zhou, Abdur Rasool, Shahbaz Siddeeq

“…Current recommendation systems often struggle to incorporate high-dimensional visual data into graph-based learning models effectively. …”
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Spectro-Image Analysis with Vision Graph Neural Networks and Contrastive Learning for Parkinson’s Disease Detection by Nuwan Madusanka, Hadi Sedigh Malekroodi, H. M. K. K. M. B. Herath, Chaminda Hewage, Myunggi Yi, Byeong-Il Lee

“…Comprehensive experimental validation on multi-institutional datasets from Italy, Colombia, and Spain demonstrates that the proposed ViG-contrastive framework achieves superior classification performance, with the ViG-M-GELU architecture achieving 91.78% test accuracy. The integration of graph neural networks with contrastive learning enables effective learning from limited labeled data while capturing complex spectro-temporal relationships that traditional Convolution Neural Network (CNN) approaches miss, representing a promising direction for developing more accurate and clinically viable speech-based diagnostic tools for PD.…”
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