MG6D: A Deep Fusion Approach for 6D Pose Estimation With Mamba and Graph Convolution Network by Jiaqi Zhu, Bin Li, Xinhua Zhao

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Anomaly Detection Based on Graph Convolutional Network–Variational Autoencoder Model Using Time-Series Vibration and Current Data by Seung-Hwan Choi, Dawn An, Inho Lee, Suwoong Lee

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DAC-GCN: A Dual Actor-Critic Graph Convolutional Network with Multi-Hop Aggregation for Enhanced Recommender Systems by Gholamreza Zare, Nima Jafari, Mehdi Hosseinzadeh, Amir Sahafi

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Multi-sensor near-realtime burnt area monitoring using a superpixel-based graph convolutional network approach by Michael Nolde, Moritz Rösch, Torsten Riedlinger, Hannes Taubenböck

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GCN-Transformer: Graph Convolutional Network and Transformer for Multi-Person Pose Forecasting Using Sensor-Based Motion Data by Romeo Šajina, Goran Oreški, Marina Ivašić-Kos

“…This paper introduces GCN-Transformer, a novel model for multi-person pose forecasting that leverages the integration of Graph Convolutional Network and Transformer architectures. …”
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Data-Driven Dynamic Graph Convolution Transformer Network Model for EEG Emotion Recognition Under IoMT Environment by Xing Jin, Fa Zhu, Yu Shen, Gwanggil Jeon, David Camacho

“…Moreover, the graph convolution operations can effectively exploit the spatial information between different channels. …”
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Graph convolutional neural networks improved target-specific scoring functions for cGAS and kRAS in virtual screening by Bo Wang, Muhammad Junaid, Wenjin Li

Subjects: “…Graph convolutional neural networks…”
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Multi-frequency EEG and multi-functional connectivity graph convolutional network based detection method of patients with Alzheimer’s disease by Yujian Liu, Libing An, Haiqiang Yang, Shuzhi Sam Ge

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Extracting multilane roads from OpenStreetMap through graph convolutional neural network and road mesh relationship analysis by Andong Wang, Fang Wu, Xianyong Gong, Renjian Zhai, Yue Qiu, Yuyang Qi

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STAGE framework: A stock dynamic anomaly detection and trend prediction model based on graph attention network and sparse spatiotemporal convolutional network. by Ming Shi, Roznim Mohamad Rasli, Shir Li Wang

“…This paper proposes the STAGE framework, which integrates the Graph Attention Network (GAT), Variational Autoencoder (VAE), and Sparse Spatiotemporal Convolutional Network (STCN), to enhance the accuracy of stock prediction and the robustness of anomaly data detection. …”
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ConBGAT: a novel model combining convolutional neural networks, transformer and graph attention network for information extraction from scanned image by Duy Ho Vo Hoang, Huy Vo Quoc, Bui Thanh Hung

“…In this study, we introduce ConBGAT, a cutting-edge model that seamlessly integrates convolutional neural networks (CNNs), Transformers, and graph attention networks to address these shortcomings. …”
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Steganographer identification of JPEG image based on feature selection and graph convolutional representation by Qianqian ZHANG, Yi ZHANG, Hao LI, Yuanyuan MA, Xiangyang LUO

“…Aiming at the problem that the feature dimension of JPEG image steganalysis is too high, which leads to the complexity of distance calculation between users and a decrease in the identification performance of the steganographer, a method for steganographer recognition based on feature selection and graph convolutional representation was proposed.Firstly, the steganalysis features of the user’s images were extracted, and the feature subset with highseparability was selected.Then, the users were represented as a graph, and the features of users were obtained by training the graph convolutional neural network.Finally, because inter-class separability and intra-class aggregation were considered, the features of users that could capture the differences between users were learned.For steganographers who use JPEG steganography, such as nsF5, UED, J-UNIWARD, and so on, to embed secret information in images, the proposed method can reduce the feature dimensions and computing.The identification accuracy of various payloads can reach more than 80.4%, and it has an obvious advantage at the low payload.…”
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Steganographer identification of JPEG image based on feature selection and graph convolutional representation by Qianqian ZHANG, Yi ZHANG, Hao LI, Yuanyuan MA, Xiangyang LUO

“…Aiming at the problem that the feature dimension of JPEG image steganalysis is too high, which leads to the complexity of distance calculation between users and a decrease in the identification performance of the steganographer, a method for steganographer recognition based on feature selection and graph convolutional representation was proposed.Firstly, the steganalysis features of the user’s images were extracted, and the feature subset with highseparability was selected.Then, the users were represented as a graph, and the features of users were obtained by training the graph convolutional neural network.Finally, because inter-class separability and intra-class aggregation were considered, the features of users that could capture the differences between users were learned.For steganographers who use JPEG steganography, such as nsF5, UED, J-UNIWARD, and so on, to embed secret information in images, the proposed method can reduce the feature dimensions and computing.The identification accuracy of various payloads can reach more than 80.4%, and it has an obvious advantage at the low payload.…”
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Research on Multi-Scale Spatio-Temporal Graph Convolutional Human Behavior Recognition Method Incorporating Multi-Granularity Features by Yulin Wang, Tao Song, Yichen Yang, Zheng Hong

Subjects: “…graph convolutional network…”
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ARO-GNN: Adaptive relation-optimized graph neural networks by Yong Lu, Zhengguo Lin

Subjects: “…Graph neural networks…”
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Constrained Heat Kernel Graph Diffusion Convolution: A High-Dimensional Statistical Approximation via Information Theory by Zhuo-Chen He

“…Their success largely stems from the powerful information propagation process. Among these networks, diffusion-based approaches, such as generalized graph diffusion convolution, have extended conventional immediate neighborhood aggregation function to a diffusion process based on Newton’s law of cooling. …”
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Dual-Gated Graph Convolutional Recurrent Unit with Integrated Graph Learning (DG3L): A Novel Recurrent Network Architecture with Dynamic Graph Learning for Spatio-Temporal Predictions by Yuxuan Wang, Zhouyuan Zhang, Shu Pi, Haishan Zhang, Jiatian Pi

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A novel encrypted traffic detection model based on detachable convolutional GCN-LSTM by Xiaogang Yuan, Jianxin Wan, Dezhi An, Huan Pei

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A Spatio-Temporal Joint Diagnosis Framework for Bearing Faults via Graph Convolution and Attention-Enhanced Bidirectional Gated Networks by Zhiguo Xiao, Xinyao Cao, Huihui Hao, Siwen Liang, Junli Liu, Dongni Li

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A topology-guided high-quality solution learning framework for security-constraint unit commitment based on graph convolutional network by Liqian Gao, Lishen Wei, Shichang Cui, Jiakun Fang, Xiaomeng Ai, Wei Yao, Jinyu Wen

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