Accurate prediction of protein–ligand interactions by combining physical energy functions and graph-neural networks by Yiyu Hong, Junsu Ha, Jaemin Sim, Chae Jo Lim, Kwang-Seok Oh, Ramakrishnan Chandrasekaran, Bomin Kim, Jieun Choi, Junsu Ko, Woong-Hee Shin, Juyong Lee

“…Our approach combines the strengths of graph neural networks with physics-based scoring methods. …”
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Insights into gait performance in Parkinson's disease via latent features of deep graph neural networks by Jiecheng Wu, Jiecheng Wu, Ning Su, Xinjin Li, Xinjin Li, Chao Yao, Jipeng Zhang, Xucheng Zhang, Wei Sun

“…However, most of the current methods depend on data preprocessing and feature engineering, often require domain knowledge and laborious human involvement, and require additional manual adjustments when dealing with new tasks.MethodsTo reduce the model's reliance on data preprocessing, feature engineering, and traversal rules, we employed the Spatial-Temporal Graph Convolutional Networks (ST-GCN) model. We also defined five distinct states within a complete gait cycle: standstill (S), left swing (L), double support (D), right swing (R), and turnaround (T). …”
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ET-GNN: Ensemble Transformer-Based Graph Neural Networks for Holistic Automated Essay Scoring by Hind Aljuaid, Areej Alhothali, Ohoud Alzamzami, Hussein Assalahi, Tahani Aldosemani

“…This paper presents a hybrid method, called Ensemble Transformer-Based Graph Neural Networks (ET-GNN), which integrates Transformer-based models with Graph Convolutional Networks (GCNs) for holistic AES. …”
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MFHG-DDI: An Enhanced Hybrid Graph Method Leveraging Multiple Features for Predicting Drug–Drug Interactions by Xiang Li, Xiangmin Ji, Chengzhen Xu, Jie Hou, Xiaoyu Zhao, Guodong Peng

“…We employed an enhanced hybrid graph module that integrates a graph convolutional network, graph attention network, and global average pooling to learn latent features, ultimately applying a prediction function to predict DDIs. …”
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Graph-Attention Diffusion for Enhanced Multivariate Time-Series Anomaly Detection by Vadim Lanko, Ilya Makarov

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Epileptic Seizure Detection in Neonatal EEG Using a Multi-Band Graph Neural Network Model by Lihan Tang, Menglian Zhao

“…In addition, a novel classification model, namely the multi-band graph neural network (MBGNN), is proposed, which utilizes the attention mechanism and can take full advantage of the multi-band graph representations to improve the classification performance. …”
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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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Infant cry classification using an efficient graph structure and attention-based model by Qiao X., Jiao S., Li H.

“…Additionally, in order to better classify the efficient graph structure, a local-to-global convolutional neural network (AlgNet) based on convolutional neural networks and attention mechanisms is proposed. …”
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A Spatiotemporal Attention-Guided Graph Neural Network for Precise Hyperspectral Estimation of Corn Nitrogen Content by Feiyu Lu, Boming Zhang, Yifei Hou, Xiao Xiong, Chaoran Dong, Wenbo Lu, Liangxue Li, Chunli Lv

“…A hyperspectral maize nitrogen content prediction model is proposed, integrating a dynamic spectral–spatiotemporal attention mechanism with a graph neural network, with the aim of enhancing the accuracy and stability of nitrogen estimation. …”
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A Novel Model for Predicting PM2.5 Concentrations Utilizing Graph Convolutional Networks and Transformer by Yuan Huang, Feilong Han, Qimeng Feng

“…In response, this paper proposes a new approach based on Graph Convolutional Networks (GCN) and Transformer. …”
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TVGeAN: Tensor Visibility Graph-Enhanced Attention Network for Versatile Multivariant Time Series Learning Tasks by Mohammed Baz

“…This paper introduces Tensor Visibility Graph-enhanced Attention Networks (TVGeAN), a novel graph autoencoder model specifically designed for MTS learning tasks. …”
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Research on the Automatic Multi-Label Classification of Flight Instructor Comments Based on Transformer and Graph Neural Networks by Zejian Liang, Yunxiang Zhao, Mengyuan Wang, Hong Huang, Haiwen Xu

“…This approach enhances the consistency and efficiency of flight training assessments and provides new insights into integrating natural language processing and graph neural networks, demonstrating broad application prospects.…”
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Spatio-temporal graph neural networks for power prediction in offshore wind farms using SCADA data by S. Daenens, S. Daenens, S. Daenens, T. Verstraeten, T. Verstraeten, T. Verstraeten, P.-J. Daems, P.-J. Daems, A. Nowé, J. Helsen, J. Helsen, J. Helsen

“…The wind farm is represented as a graph, with graph neural networks (GNNs) used to aggregate selected input features from neighboring turbines. …”
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An edge server placement based on graph clustering in mobile edge computing by Shanshan Zhang, Jiong Yu, Mingjian Hu

“…The model mainly consists of a two-layer graph convolutional network (GCN) component and a differentiable version of K-means clustering component, which transforms the server placement problem into an end-to-end learning optimization problem on a graph. …”
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Combination of Graph and Convolutional Networks for Brain Tumor Segmentation from Multi-Modal MR Images In Clinical Applications by Marjan Vatanpour, Javad Haddadnia, Shahryar Salmani Bajestani

“…The novel architecture uses a simple Convolutional Neural Network (CNN) and Graph Neural Network (GNN) sequentially. …”
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A hybrid adversarial autoencoder-graph network model with dynamic fusion for robust scRNA-seq clustering by Binhua Tang, Yingying Feng, Xinyu Gao

“…Results Here, we present a novel deep clustering method, scCAGN, based on an adversarial autoencoder (AAE) and a cross-attention graph convolutional network (GCN), to address the above challenges in scRNA-seq data analysis. …”
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Combining Long-Term Recurrent Convolutional and Graph Convolutional Networks to Detect Phishing Sites Using URL and HTML by Subhash Ariyadasa, Shantha Fernando, Subha Fernando

“…This paper proposes PhishDet, a new way of detecting phishing websites through Long-term Recurrent Convolutional Network and Graph Convolutional Network using URL and HTML features. …”
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A Multiscale Mixed-Graph Neural Network Based on Kinematic and Dynamic Joint Features for Human Motion Prediction by Rongyong Zhao, Bingyu Wei, Lingchen Han, Yuxin Cai, Yunlong Ma, Cuiling Li

“…Furthermore, to extract both kinematic and dynamic joint information effectively for predicting long-term human motion, we propose a Multiscale Mixed-Graph Neural Network (MS-MGNN). MS-MGNN can extract kinematic and dynamic joint features across three distinct scales: joints, limbs, and body parts. …”
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Graph theory-based analysis of functional connectivity changes in brain networks underlying cognitive fatigue: An EEG study. by Yabing Lou, Rui Pi, Ruifeng Sun, Jilin Wu, Wei Wang, Ziman Zhu, Tengteng Dai, Weijun Gong

“…Through the application of both global and local graph-theoretical metrics to characterize the topology of brain networks, this study establishes a conceptual framework supporting enhanced detection of cognitive fatigue manifestations while facilitating examination of its neurophysiological substrates.…”
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Correlations between an Urban Three-Dimensional Pedestrian Network and Service Industry Layouts Based on Graph Convolutional Neural Networks: A Case Study of Xinjiekou, Nanjing by Xinyu Hu, Ruxia Bai, Chen Li, Beixiang Shi, Hui Wang

“…Focusing on catering formats, we introduced a method to study the spatial distribution characteristics of service industries in three-dimensional spaces and employed a graph convolutional network model to systematically analyze the correlation between pedestrian network closeness and betweenness with catering formats. …”
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