Efficient Visual-Aware Fashion Recommendation Using Compressed Node Features and Graph-Based Learning by Umar Subhan Malhi, Junfeng Zhou, Abdur Rasool, Shahbaz Siddeeq

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VE-GCN: A Geography-Aware Approach for Polyline Simplification in Cartographic Generalization by Siqiong Chen, Anna Hu, Yongyang Xu, Haitao Wang, Zhong Xie

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2D Spatiotemporal Hypergraph Convolution Network for Dynamic OD Traffic Flow Prediction by Cheng Fang, Li Wang

“…Initially, temporal characteristics of traffic flow between OD pairs are captured using a 1D convolution neural network (1D-CNNs). Subsequently, a 2D hypergraph convolutional network is introduced to uncover spatial correlations in OD flow patterns. …”
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Video Tactical Intelligence Analysis Method of Karate Competition Based on Convolutional Neural Network by Jun Zhong, Jian Xu

“…Therefore, based on the convolutional neural network, this paper establishes a new graph convolution model for automatic intelligent analysis of karate athletes’ technical action recognition, action frequency statistics, and trajectory tracking. …”
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Cyber security entity recognition method based on residual dilation convolution neural network by Bo XIE, Guowei SHEN, Chun GUO, Yan ZHOU, Miao YU

“…In recent years,cybersecurity threats have increased,and data-driven security intelligence analysis has become a hot research topic in the field of cybersecurity.In particular,the artificial intelligence technology represented by the knowledge graph can provide support for complex cyberattack detection and unknown cyberattack detection in multi-source heterogeneous threat intelligence data.Cybersecurity entity recognition is the basis for the construction of threat intelligence knowledge graphs.The composition of security entities in open network text data is very complex,which makes traditional deep learning methods difficult to identify accurately.Based on the pre-training language model of BERT (pre-training of deep bidirectional transformers),a cybersecurity entity recognition model BERT-RDCNN-CRF based on residual dilation convolutional neural network and conditional random field was proposed.The BERT model was used to train the character-level feature vector representation.Combining the residual convolution and the dilation neural network model to effectively extract the important features of the security entity,and finally obtain the BIO annotation of each character through CRF.Experiments on the large-scale cybersecurity entity annotation dataset constructed show that the proposed method achieves better results than the LSTM-CRF model,the BiLSTM-CRF model and the traditional entity recognition model.…”
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MHCAGAT: A Meta Hybrid Convolution Attention Network for Urban Traffic Flow Prediction by Yu Zhan, Suzi Iryanti Fadilah, Azizul Rahman Mohd Shariff

“…To address these issues, a novel traffic prediction model is proposed, Meta Hybrid Convolution Attention Graph Attention Network (MHCAGAT). …”
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DGNMDA: Dual Heterogeneous Graph Neural Network Encoder for miRNA-Disease Association Prediction by Daying Lu, Qi Zhang, Chunhou Zheng, Jian Li, Zhe Yin

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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

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A rhetorical structure theory and inference-aware graph network for emotion-cause pair extraction by Qi Zhang, Botao Wang, Peican Zhu, Junchao Zhao, Kexin Liu

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An Advanced Spatio-Temporal Graph Neural Network Framework for the Concurrent Prediction of Transient and Voltage Stability by Chaoping Deng, Liyu Dai, Wujie Chao, Junwei Huang, Jinke Wang, Lanxin Lin, Wenyu Qin, Shengquan Lai, Xin Chen

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Hyperledger Fabric Graph Isomorphism Network for Conflict Transactions Detection in Multi-Version Concurrency Control by Fawaz Alzahrani, Mohd Yazid Idris, Mohd Fo'Ad Rohani, Rahmat Budiarto

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Corrigendum to: “Bearing fault diagnosis based on a multiple-constraint modal-invariant graph convolutional fusion network” [High-speed Railway 2 (2024) 92–100] by Zhongmei Wang, Pengxuan Nie, Jianhua Liu, Jing He, Haibo Wu, Pengfei Guo

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Correction: Oluwasanmi et al. Multi-Head Spatiotemporal Attention Graph Convolutional Network for Traffic Prediction. <i>Sensors</i> 2023, <i>23</i>, 3836 by Ariyo Oluwasanmi, Muhammad Umar Aftab, Zhiguang Qin, Muhammad Shahzad Sarfraz, Yang Yu, Hafiz Tayyab Rauf

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An edge server placement based on graph clustering in mobile edge computing by Shanshan Zhang, Jiong Yu, Mingjian Hu

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A Representation-Learning-Based Graph and Generative Network for Hyperspectral Small Target Detection by Yunsong Li, Jiaping Zhong, Weiying Xie, Paolo Gamba

Subjects: “…Graph Convolutional Network…”
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Topology-Guided Graph Masked Autoencoder Learning for Population-Based Neurodevelopmental Disorder Diagnosis by Yueying Li, Xiaotong Zhang, Shihan Guan, Guolin Ma, Youyong Kong

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YOLO-BCD: A Lightweight Multi-Module Fusion Network for Real-Time Sheep Pose Estimation by Chaojie Sun, Junguo Hu, Qingyue Wang, Chao Zhu, Lei Chen, Chunmei Shi

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Predicting drug combination side effects based on a metapath-based heterogeneous graph neural network by Leixia Tian, Qi Wang, Zhiheng Zhou, Xiya Liu, Ming Zhang, Guiying Yan

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Global information aware network with global interaction graph attention for infrared small target detection by Ruimin Yang, Yidan Zhang, Guangshuai Gao, Liang Liao, Chunlei Li

“…To address these challenges, this study proposes a novel global information‐aware network with global interaction graph attention (GIGA) for infrared small target detection. …”
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An automatic ICD coding method for clinical records based on deep neural network by Yichao DU, Tong XU, Jianhui MA, Enhong CHEN, Yi ZHENG, Tongzhu LIU, Guixian TONG

Subjects: “…ICD coding;multi-scale;residual network;graph convolutional network…”
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