Suchergebnisse - Graph Neural Network Models and Applications

A Novel Global Prototype-Based Node Embedding Technique von Zyad Alkayem, Rami Zewail, Amin Shoukry, Daisuke Kawahara, Samir A. Elsagheer Mohamed

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Application of Graph Neural Networks to Model Stem Cell Donor–Recipient Compatibility in the Detection and Classification of Leukemia von Eltanashi, Saeeda Meftah Salem, Kurnaz Türkben, Ayça

“… Machine learning models like support vector machine (SVM), convolutional neural networks (CNNs …”
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Guest Editorial: Deep Neural Networks for Graphs: Theory, Models, Algorithms, and Applications von Li, Ming, Micheli, Alessio, Wang, Yu Guang, Pan, Shirui, Lio, Pietro, Gnecco, Giorgio Stefano, Sanguineti, Marcello

“… Deep neural networks for graphs (DNNGs) represent an emerging field that studies how the deep learning method can be generalized to graph-structured data …”
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A graph neural network model application in point cloud structure for prolonged sitting detection system based on smartphone sensor data von Mardi Hardjianto, Jazi Eko Istiyanto, A. Min Tjoa, Arfa Shaha Syahrulfath, Satriawan Rasyid Purnama, Rifda Hakima Sari, Zaidan Hakim, M. Ridho Fuadin, Nias Ananto

“… or accommodate the often incomplete or unstructured nature of healthcare data. To address this gap, our study introduces a novel application of graph neural networks (GNNs …”
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A graph neural network model application in point cloud structure for prolonged sitting detection system based on smartphone sensor data von Hardjianto, Mardi, Istiyanto, Jazi Eko, Tjoa, A. Min, Syahrulfath, Arfa Shaha, Purnama, Satriawan Rasyid, Sari, Rifda Hakima, Hakim, Zaidan, Fuadin, M. Ridho, Ananto, Nias

“… or accommodate the often incomplete or unstructured nature of healthcare data. To address this gap, our study introduces a novel application of graph neural networks (GNNs …”
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A survey of geometric graph neural networks: data structures, models and applications von HAN, Jiaqi, CEN, Jiacheng, WU, Liming, LI, Zongzhao, KONG, Xiangzhe, JIAO, Rui, YU, Ziyang, XU, Tingyang, WU, Fandi, WANG, Zihe, XU, Hongteng, WEI, Zhewei, ZHAO, Deli, LIU, Yang, RONG, Yu, HUANG, Wenbing

“… Unlike generic graphs, geometric graphs often exhibit physical symmetries of translations, rotations, and reflections, making them ineffectively processed by current Graph Neural Networks (GNNs …”
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An Application of Graph Neural Network Model Design for Residential Building Layout Design von Wang, Shiyu, Wang, Ningbo

“… To address these issues, a residential building layout design method based on graph neural network model is proposed to improve the intelligence level of residential building layout design …”
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Application of Graph Neural Network in Matching Intangible Cultural Heritage Models with Virtual Reality Scenes von Guan, Jing

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A Graph Neural Network Node Classification Application Model with Enhanced Node Association von Zhang, Yuhang, Xu, Yaoqun, Zhang, Yu

“… In this paper, we propose the graph neural network model ENode-GAT for improving the accuracy of small sample node classification using the method of external referencing of similar word nodes …”
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Estimating travel time in transport network with a combined multi-attributed graph convolutional neural network and multilayer perceptron model von Betkier, Igor

“… In this article, an advanced model for forecasting travel time in road networks is presented, employing a Graph Convolutional Neural Network (GCN …”
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A review of graph neural networks: concepts, architectures, techniques, challenges, datasets, applications, and future directions von Khemani, Bharti, Patil, Shruti, Kotecha, Ketan, Tanwar, Sudeep

“… Different applications may require various graph neural network (GNN) models. GNNs facilitate the exchange of information between nodes in a graph, enabling them to understand dependencies within the nodes and edges …”
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On the use of mechanics-informed models to structural engineering systems: Application of graph neural networks for structural analysis von Parisi, Fabio, Ruggieri, Sergio, Lovreglio, Ruggiero, Fanti, Maria Pia, Uva, Giuseppina

“… This paper investigates the application of mechanics-informed artificial intelligence to civil structural systems …”
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LQ-GNN: A Graph Neural Network Model for Response Time Prediction of Microservice-Based Applications in the Computing Continuum von Richart, Matias, Gorricho, Juan-Luis, Baliosian, Javier, Contreras, Luis M., Muniz, Alejandro, Serrat, Joan

“… ) modeling with Graph Neural Networks (GNN). LQ-GNN allows to efficiently estimate the response time of applications under different resource allocations and placements on the computing …”
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Melting temperature prediction using a graph neural network model: From ancient minerals to new materials von Hong, Qi-Jun, Ushakov, Sergey V, van de Walle, Axel, Navrotsky, Alexandra

“… The model, made publicly available online, features graph neural network and residual neural network architectures …”
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Generative AI-Driven Liver Reconstruction for Healthcare Applications in Consumer Electronics with Diffusion Model and Graph Neural Network von Xu, Xu, Yang, Jing, Liu, Xiaoli, Khan, Muhammad Attique, Jiang, Weiwei, Baili, Jamel, Yee, Por Lip, Li, Congsheng

“… To overcome these challenges, we present a generative AI framework that integrates a conditional diffusion model with a graph neural network (GNN …”
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Application of Graph Convolutional Neural Networks Combined with Single-Model Decision-Making Fusion Neural Networks in Structural Damage Recognition von Li, Xiaofei, Xu, Langxing, Guo, Hainan, Yang, Lu

“… Graph convolutional neural networks (GCNs), unlike other methods, have the ability to learn the spatial characteristics of the sensors, which is targeted at the above problems in structural damage identification …”
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Graph Neural Networks for Evaluating the Reliability and Resilience of Infrastructure Systems: A Systematic Review of Models, Applications and Future Directions von Liu, Tong, Liu, Fangyu

“… from natural disasters and human-induced events. Graph neural networks (GNNs) have emerged as a powerful tool for addressing these challenges, offering data-driven …”
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Conceptual-temporal graph convolutional neural network model for stock price movement prediction and application von Fuping, Zhang

“… convolutional neural network model (CT-GCNN) is designed to map the linkage effect and predict the stock price movement …”
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Self-learning dynamic graph neural network with self-attention based on historical data and future data for multi-task multivariate residential air conditioning forecasting von Hu, Zehuan, Gao, Yuan, Sun, Luning, Mae, Masayuki, Imaizumi, Taiji

“… Addressing the gaps in the existing graph neural network applications, this model overcomes the limitations of static graph structures by constructing evolving adjacency matrices integrated …”
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Attention Mechanism-Based Graph Neural Network Model for Effective Activity Prediction of SARS-CoV-2 Main Protease Inhibitors: Application to Drug Repurposing as Potential COVID-19 Therapy von Wu, Yanling, Li, Kun, Li, Menglong, Pu, Xuemei, Guo, Yanzhi

“… Here, we present a graph neural network-based deep-learning (DL) strategy to prioritize the existing drugs for their potential therapeutic effects against SARS-CoV-2 Mpro …”
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