Dynamic graph attention network for local leisure event recommendation in event-based social networks by Xia Peng, Yazhao Wu, Zhiming Gui, Yitian Liu, Zhou Huang, Yi Bao

“…To address these issues, we propose ERDGAT, a dynamic graph attention network model for event recommendations in EBSNs. …”
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Analysis of Persian News Agencies on Instagram, A Words Co-occurrence Graph-based Approach by Mohammad Heydari, Babak Teimourpour

“…The inherent structure of Instagram, characterized by its text-rich content and graph-like data representation, enables the utilization of text and graph processing techniques for data analysis purposes. …”
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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

“…In this paper, we present the Visual-aware Graph Convolutional Network (VAGCN). This novel framework helps improve how visual features can be incorporated into graph-based learning systems for fashion item compatibility predictions. …”
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Investigating and Optimizing MINDWALC Node Classification to Extract Interpretable Decision Trees from Knowledge Graphs by Maximilian Legnar, Joern-Helge Heinrich Siemoneit, Gilles Vandewiele, Jürgen Hesser, Zoran Popovic, Stefan Porubsky, Cleo-Aron Weis

“…This work deals with the investigation and optimization of the MINDWALC node classification algorithm with a focus on its ability to learn human-interpretable decision trees from knowledge graph databases. For this, we introduce methods to optimize MINDWALC for a specific use case, in which the processed knowledge graph is strictly divided into its inner <i>background knowledge</i> (knowledge about a given domain) and <i>instance knowledge</i> (knowledge about given instances). …”
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Center of Trapezoid Graph: Application in Selecting Center Location to Set up a Private Hospital by Shaoli Nandi, Sukumar Mondal, Sovan Samanta, Sambhu Charan Barman, Leo Mrsic, Antonios Kalampakas

“…We also find an algorithmic solution to real problems (that involves finding a center location in a district to build a private hospital that minimizes the farthest distance from it to all areas of the district) with the help of the trapezoid graph model and BFS trees within <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>O</mi><mo>(</mo><mi>n</mi><mo>)</mo></mrow></semantics></math></inline-formula> time.…”
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Graph-based SLAM using wall detection and floor plan constraints without loop closure by Masahiko Hoshi, Yoshitaka Hara, Sousuke Nakamura

“…Abstract This paper describes a graph-based SLAM approach using wall detection and floor plan constraints without relying on loop closure. …”
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Intelligent accounting question-answering robot based on a large language model and knowledge graph by Shi Shengyun, Li Guoxi, Wang Yong

“…This study aims to design an intelligent accounting question-answering robot based on a large language model and knowledge graph. To build a complete knowledge graph, this study uses the attention mechanism and convolutional neural network to build a connection prediction model and completes the accounting question-answering knowledge graph. …”
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Using graph machine learning to identify functioning in patients with low back pain in terms of ICF by Linda Nieminen, Harri Ketamo, Jari Vuori, Markku Kankaanpää

“…However, its complex structure poses a problem for implementation as part of clinical practice.The aim of this study was to test a graph machine learning engine, Headai Graphmind, to recognize ICF codes from electronic health records written in Finnish. …”
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The analysis of artificial intelligence knowledge graphs for online music learning platform under deep learning by Shen Jiang, Ningning Shi, Chang Liu

“…The knowledge graph provides the platform with rich semantic information and relational data, helping the model better understand the correlation between user needs and music content, thereby improving the accuracy and personalization of recommendation results. …”
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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

“…ObjectivesIn order to solve the problems of low efficiency of fault feature extraction, inaccurate feature representation, and difficulty in adapting existing methods to complex signal requirements in wind turbine bearing fault diagnosis, a fault diagnosis method based on graph regularization was proposed. The method helps to improve the analysis ability of vibration signals, thereby achieving accurate classification and reliable diagnosis of different fault types.MethodsThe technology based on graph regularization auto-encoder was adopted, combined with the idea of graph embedding, to guide the stack-supervised auto-encoder to carry out feature extraction. …”
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Domain‐adaptation‐based named entity recognition with information enrichment for equipment fault knowledge graph by Dengrui Xiong, Xinyu Li, Liang Gao, Yiping Gao

“…Update of word segmentation dictionary and adjustment of masking approach are implemented during DAPT for information enrichment, which helps make the most of the limited domain‐specific pretraining corpus. …”
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Fast Algorithm for Cyber-Attack Estimation and Attack Path Extraction Using Attack Graphs with AND/OR Nodes by Eugene Levner, Dmitry Tsadikovich

“…Determining an attacker’s possible attack path is critical to cyber defenders as it helps identify threats, harden the network, and thwart attacker’s intentions. …”
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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

“…This method frames the radar task collaborative scheduling problem as a heterogeneous network graph, leveraging model knowledge to optimize the training process of the Graph Neural Network (GNN) algorithm. …”
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Advanced biomedical imaging for identifying blood cell type: Integrating segmentation, feature extraction, and GraphSAGE model by Nur Mohammad Fahad, Mohaimenul Azam Khan Raiaan, Arefin Ittesafun Abian, Ripon Kumar Debnath, Sidratul Montaha, Mirjam Jonkman, Sami Azam

“…Feature ranking analysis identifies optimal features and reduces dimensionality, aiding graph dataset construction based on data similarity. …”
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The relationship between economic growth and carbon emissions based on the combination of graph neural network and wavelet transform by Sen Wang

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Accuracy of StepWatch™ and ActiGraph accelerometers for measuring steps taken among persons with multiple sclerosis. by Brian M Sandroff, Robert W Motl, Lara A Pilutti, Yvonne C Learmonth, Ipek Ensari, Deirdre Dlugonski, Rachel E Klaren, Swathi Balantrapu, Barry J Riskin

“…The StepWatch had better accuracy (99.0%) than the ActiGraph (95.5%) in the overall sample under the SWS condition, and this was particularly apparent in those with severe disability (StepWatch: 95.7%; ActiGraph: 87.3%). …”
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Research on safety risk assessment model of construction engineering based on attention mechanism and graph neural network by Lanfei He, Ran Chen, Jia Hu, Zhenxi Huang, Li Zhou, Hong Zhang

“…This paper profoundly studies the construction engineering safety risk assessment model based on attention mechanism and graph neural network, aiming at improving the accuracy and timeliness of construction site safety risk early warning. …”
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Identifying key psychological characteristics among Chinese individuals with eating disorders: an exploratory graph and network analysis by Liyun Zheng, Chao Chen, Darong Zhang, Xueni Li, Shuxia Geng, Qingmei Kong, Tianmei Si

“…This study combines exploratory graph analysis (EGA) and network analysis to identify key psychological characteristics in Chinese patients with EDs. …”
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Connectogram-COH: A Coherence-Based Time-Graph Representation for EEG-Based Alzheimer’s Disease Detection by Ehssan Aljanabi, İlker Türker

“…<b>Methods</b>: This study proposes a new transformation strategy that generates a graph representation with time resolution, which handles EEG recordings as relatively small time windows and converts these segments into a similarity graph based on signal coherence between available channels. …”
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Prediction Model for Soybean Productivity by Ion GANEA

“…Among these tech-nologies are graph database systems such as Neo4j, which have demonstrated success in predicting the studied phenomena. …”
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