Few-shot English text classification method based on graph convolutional network and prompt learning by Yunfei Jin

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MSASGCN :  Multi-Head Self-Attention Spatiotemporal Graph Convolutional Network for Traffic Flow Forecasting by Yang Cao, Detian Liu, Qizheng Yin, Fei Xue, Hengliang Tang

“…The multi-head self-attention mechanism is a valuable method to capture dynamic spatial-temporal correlations, and combining it with graph convolutional networks is a promising solution. …”
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Traffic flow prediction based on spatial-temporal multi factor fusion graph convolutional networks by Ying-Ting Chen, An Liu, Cheng Li, Shuang Li, Xiao Yang

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Node-Based Graph Convolutional Network With SLIC Method for Breast Cancer Ultrasound Images Classification by Kien Trang, Fung Fung Ting, Bao Quoc Vuong, Chee-Ming Ting

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A Dynamic Multi-Graph Convolutional Spatial-Temporal Network for Airport Arrival Flow Prediction by Yunyang Huang, Hongyu Yang, Zhen Yan

“…Specifically, in the spatial dimension, a novel dynamic multi-graph convolutional network is designed to adaptively model the heterogeneous and dynamic airport networks. …”
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Temporal representation learning enhanced dynamic adversarial graph convolutional network for traffic flow prediction by Linlong Chen, Linbiao Chen, Hongyan Wang, Jian Zhao

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Resilient Temporal Graph Convolutional Network for Smart Grid State Estimation Under Topology Inaccuracies by Seyed Hamed Haghshenas, Mia Naeini

“…This paper studies these scenarios under topology uncertainties and evaluates the impact of the topology uncertainties on the performance of a Temporal Graph Convolutional Network (TGCN) for state estimation in power systems. …”
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Spatial-temporal upsampling graph convolutional network for daily long-term traffic speed prediction by Song Zhang, Yanbing Liu, Yunpeng Xiao, Rui He

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Classification of Pulmonary Nodules Using Multimodal Feature‐Driven Graph Convolutional Networks with Specificity Proficiency by Renjie Xu, Zhanlue Liang, Dan Wang, Rui Zhang, Jiayi Li, Lingfeng Bi, Kai Zhang, Weimin Li

“…Compared with radiomics and clinical feature‐based machine learning methods, whether a graph convolutional neural network (GCNN) based on radiomics and clinical features improve the performance in distinguishing benign and malignant pulmonary nodules is not well studied. …”
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Incorporating edge convolution and correlative self-attention into graph neural network for material properties prediction by Zexi Yang, Qi Yu, Yapeng Zhan, Jiying Liu

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Enhanced Wind Power Forecasting Using Graph Convolutional Networks with Ramp Characterization and Error Correction by Xin He, Yichen Ma, Jiancang Xie, Gang Zhang, Tuo Xie

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Multisource Data Fusion With Graph Convolutional Neural Networks for Node-Level Traffic Flow Prediction by Lei Huang, Jianxin Qin, Tao Wu

“…This paper introduces a multisource data fusion approach with graph convolutional neural networks (GCNs) for node-level traffic flow prediction. …”
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Multi-station water level forecasting using advanced graph convolutional networks with adversarial learning by Xinhai Han, Xiaohui Li, Jingsong Yang, Jiuke Wang, Guoqi Han, Jun Ding, Hui Shen, Jun Yan, Dake Chen

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Spotting Leaders in Organizations with Graph Convolutional Networks, Explainable Artificial Intelligence, and Automated Machine Learning by Yunbo Xie, Jose D. Meisel, Carlos A. Meisel, Juan Jose Betancourt, Jianqi Yan, Roberto Bugiolacchi

“…State-of-the-art performance is obtained using various statistical machine learning methods, graph convolutional networks (GCN), automated machine learning (AutoML), and explainable artificial intelligence (XAI). …”
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Spatiotemporal Flood Hazard Classification in Bangkok Using Graph Convolutional Network and Temporal Fusion Transformer by Pakpoom Chaimook, Nirattaya Khamsemanan, Cholwich Nattee, Alice Sharp

Subjects: “…Graph convolution network…”
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AdaptedNorm: An Adaptive Modeling Strategy for Graph Convolutional Network-Based Deep Learning Tasks by Chuan Dai, Yajuan Wei, Hao Wang, Ying Liu, Zhijie Xu

“…Graph neural networks (GNNs), particularly graph convolutional networks (GCNs), have demonstrated remarkable success in modeling graph-structured data across diverse applications. …”
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Auxiliary Task Graph Convolution Network: A Skeleton-Based Action Recognition for Practical Use by Junsu Cho, Seungwon Kim, Chi-Min Oh, Jeong-Min Park

“…Graph convolution networks (GCNs) have been extensively researched for action recognition by estimating human skeletons from video clips. …”
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EHC-GCN: Efficient Hierarchical Co-Occurrence Graph Convolution Network for Skeleton-Based Action Recognition by Ying Bai, Dongsheng Yang, Jing Xu, Lei Xu, Hongliang Wang

“…In tasks such as intelligent surveillance and human–computer interaction, developing rapid and effective models for human action recognition is crucial. Currently, Graph Convolution Networks (GCNs) are widely used for skeleton-based action recognition. …”
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Edge and Node Enhancement Graph Convolutional Network: Imbalanced Graph Node Classification Method Based on Edge-Node Collaborative Enhancement by Jiadong Tian, Jiali Lin, Dagang Li

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STFDSGCN: Spatio-Temporal Fusion Graph Neural Network Based on Dynamic Sparse Graph Convolution GRU for Traffic Flow Forecast by Jiahao Chang, Jiali Yin, Yanrong Hao, Chengxin Gao

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