Linear attention based spatiotemporal multi graph GCN for traffic flow prediction by Yanping Zhang, Wenjin Xu, Benjiang Ma, Dan Zhang, Fanli Zeng, Jiayu Yao, Hongning Yang, Zhenzhen Du

“…This study introduces the Linear Attention Based Spatial-Temporal Multi-Graph Convolutional Neural Network (LASTGCN), a novel deep learning model tailored for traffic flow prediction. …”
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Research on risk assessment and optimization of GCN supply chain financial network based on M estimation by Xinquan Yu, Feide Tong, Zhongzhen Hu

“…Therefore, this study proposes a graph convolutional network (GCN) model based on M estimation for risk assessment and optimization of supply chain financial networks. …”
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Advanced AI techniques for classifying Alzheimer’s disease and mild cognitive impairment by Sophie Tascedda, Sophie Tascedda, Sophie Tascedda, Pierfrancesco Sarti, Pierfrancesco Sarti, Veronica Rivi, Claudia Savia Guerrera, Giuseppe Alessio Platania, Mario Santagati, Filippo Caraci, Filippo Caraci, Johanna M. C. Blom, Johanna M. C. Blom

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KGRDR: a deep learning model based on knowledge graph and graph regularized integration for drug repositioning by Huimin Luo, Huimin Luo, Hui Yang, Hui Yang, Ge Zhang, Ge Zhang, Jianlin Wang, Jianlin Wang, Junwei Luo, Chaokun Yan, Chaokun Yan, Chaokun Yan

“…In this study, we propose a novel deep learning-based framework KGRDR containing multi-similarity integration and knowledge graph learning to predict potential drug-disease interactions. …”
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Individual Contribution-Based Spatial-Temporal Attention on Skeleton Sequences for Human Interaction Recognition by Xing Liu, Bo Gao

“…To address the above issues, we propose an innovative method by designing the individual contribution based spatial-temporal attention graph convolutional network. In this work, we first propose a simple but feasible view transformation method to reduce data mismatch from multi-view cameras. …”
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IEDSFAN: information enhancement and dynamic-static fusion attention network for traffic flow forecasting by Lianfei Yu, Ziling Wang, Wenxi Yang, Zhijian Qu, Chongguang Ren

“…Most of the existing traffic flow forecasting methods are static graph convolutional networks based on prior knowledge, ignoring the special spatial–temporal dynamics of spatial–temporal data. …”
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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

“…To address challenges in detecting fraudulent cryptocurrency transactions, MDST‐GNN integrates a multi‐distance graph convolutional architecture with adaptive temporal modeling, enabling capture of both local and global spatial dependencies while inferring patterns from anonymized temporal data. …”
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Comparative Analysis of Multi-Omics Integration Using Graph Neural Networks for Cancer Classification by Fadi Alharbi, Aleksandar Vakanski, Boyu Zhang, Murtada K. Elbashir, Mohanad Mohammed

“…This study evaluates graph neural network architectures for multi-omics (MO) data integration based on graph-convolutional networks (GCN), graph-attention networks (GAT), and graph-transformer networks (GTN). …”
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MRDDA: a multi-relational graph neural network for drug–disease association prediction by Congzhou Chen, Yaozheng Zhou, Yinghong Li, Jin Xu, Demin Li, Lingfeng Wang

“…First, we design a hybrid graph convolutional framework to capture both local and global representations of drugs and diseases. …”
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Rapid diagnosis of rheumatoid arthritis and ankylosing spondylitis based on Fourier transform infrared spectroscopy and deep learning by Wei Shuai, Xue Wu, Chen Chen, Enguang Zuo, Xiaomei Chen, Zhengfang Li, Xiaoyi Lv, Lijun Wu, Cheng Chen

“…Method: A total of 243 dried serum samples were collected in this study, including 81 samples each from ankylosing spondylitis, rheumatoid arthritis, and healthy controls. Three multi-scale convolutional modules with different specifications were designed based on the multi-scale convolutional neural network (MSCNN) to effectively fuse the local features to enhance the generalization ability of the model. …”
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Semantic Fusion-Oriented Bi-Typed Multi-Relational Heterogeneous Graph Neural Network by Yifan Sun, Jing Yan, Lilei Lu, Hongbo Zhang, Yanhong Shang

“…Compared to traditional heterogeneous graph (HG) data, Bi-typed Multi-relational Heterogeneous Graph (BMHG) not only have various edge relationships between different types of nodes but also connections among the same type of nodes, which increases modeling complexity. …”
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MTGNet: Multi-Agent End-to-End Motion Trajectory Prediction with Multimodal Panoramic Dynamic Graph by Yinfei Dai, Yuantong Zhang, Xiuzhen Zhou, Qi Wang, Xiao Song, Shaoqiang Wang

“…Additionally, it enables accurate and efficient multi-modal multi-agent trajectory prediction. In addition, we utilize the graph convolutional neural network (GCN) to process graph-structured data. …”
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Non-end-to-end adaptive graph learning for multi-scale temporal traffic flow prediction. by Kang Xu, Bin Pan, MingXin Zhang, Xuan Zhang, XiaoYu Hou, JingXian Yu, ZhiZhu Lu, Xiao Zeng, QingQing Jia

“…The method incorporates a multi-scale temporal attention module and a multi-scale temporal convolution module to extract multi-scale information. …”
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A combined model for short-term traffic flow prediction based on variational modal decomposition and deep learning by Chuanxiang Ren, Fangfang Fu, Changchang Yin, Li Lu, Lin Cheng

“…Therefore, a combined prediction model, VMD-GAT-MGTCN, based on variational modal decomposition (VMD), graph attention network (GAT), and multi-gated attention time convolutional network (MGTCN) is proposed to enhance short-term traffic flow prediction accuracy. …”
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An Adaptive Methodology for Constructing Domain-Specific Sentiment Lexicons Based on Chinese Social Media Data by Xue Xu, Haidong Liu, Lei Liu

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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

“…To address this challenge, this study presents a novel multi-label classification model that seamlessly integrates RoBERTa, a robust language model, with Graph Convolutional Networks (GCNs). …”
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A Multi-Task Spatiotemporal Graph Neural Network for Transient Stability and State Prediction in Power Systems by Shuaibo Wang, Xinyuan Xiang, Jie Zhang, Zhuohang Liang, Shufang Li, Peilin Zhong, Jie Zeng, Chenguang Wang

“…To address these challenges, this paper presents a multi-task learning framework based on spatiotemporal graph convolutional networks that efficiently performs both tasks. …”
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Graph attention networks based multi-agent path finding via temporal-spatial information aggregation. by Qingling Zhang, Peng Wang, Cui Ni, Xianchang Liu

“…An effective Multi-Agent Path Finding (MAPF) algorithm must efficiently plan paths for multiple agents while adhering to constraints, ensuring safe navigation from start to goal. …”
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Optimizing Air Pollution Forecasting Models Through Knowledge Distillation: A Novel GCN and TRANS_GRU Methodology for Indian Cities by Sudhir Kumar, Vaneet Kour, Ankit Raj, Tagru Tapung, Shivendu Mishra, Rajiv Misra, T. N. Singh

“…To address these challenges, we introduced the Graph Convolutional Networks (GCNs) for finding single and multi-dominant pollutants, and Transformer_Gated Recurrent Unit (TRANS_GRU) hybrid deep learning model for improved accuracy in the prediction of short and long-term pollutants levels subsequently applied Knowledge distillation (KD) for efficient, lightweight modelling. …”
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Med-DGTN: Dynamic Graph Transformer with Adaptive Wavelet Fusion for multi-label medical image classification by Guanyu Zhang, Yan Li, Tingting Wang, Guokun Shi, Li Jin, Zongyun Gu, Zongyun Gu

“…To address these challenges, we propose Med-DGTN, a dynamically integrated framework designed to advance multi-label classification performance in clinical imaging analytics.MethodsThe proposed Med-DGTN (Dynamic Graph Transformer Network with Adaptive Wavelet Fusion) introduces three key innovations: (1) A cross-modal alignment mechanism integrating convolutional visual patterns with graph-based semantic dependencies through conditionally reweighted adjacency matrices; (2) Wavelet-transform-enhanced dense blocks (WTDense) employing multi-frequency decomposition to amplify low-frequency pathological biomarkers; (3) An adaptive fusion architecture optimizing multi-scale feature hierarchies across spatial and spectral domains.ResultsValidated on two public medical imaging benchmarks, Med-DGTN demonstrates superior performance across modalities: (1) Achieving a mean average precision (mAP) of 70.65% on the retinal imaging dataset (MuReD2022), surpassing previous state-of-the-art methods by 2.68 percentage points. (2) On the chest X-ray dataset (ChestXray14), Med-DGTN achieves an average Area Under the Curve (AUC) of 0.841. …”
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