MSA-GCN: Exploiting Multi-Scale Temporal Dynamics With Adaptive Graph Convolution for Skeleton-Based Action Recognition by Kowovi Comivi Alowonou, Ji-Hyeong Han

“…It consists of two modules: Multi-stage Adaptive Graph Convolution (MSA-GC) and Temporal Multi-Scale Transformer (TMST). …”
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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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spaMGCN: a graph convolutional network with autoencoder for spatial domain identification using multi-scale adaptation by Tianjiao Zhang, Hongfei Zhang, Zhongqian Zhao, Saihong Shao, Yucai Jiang, Xiang Zhang, Guohua Wang

“…By integrating spatial transcriptomics and spatial epigenomic data through an autoencoder and a multi-scale adaptive graph convolutional network, spaMGCN outperforms baseline methods. …”
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MTAGCN: Multi-Task Graph-Guided Convolutional Network with Attention Mechanism for Intelligent Fault Diagnosis of Rotating Machinery by Bo Wang, Shuai Zhao

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A multi-graph convolutional network method for Alzheimer’s disease diagnosis based on multi-frequency EEG data with dual-mode connectivity by Qingjie Xu, Qingjie Xu, Libing An, Haiqiang Yang, Haiqiang Yang, Keum-Shik Hong, Keum-Shik Hong

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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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DeepMoIC: multi-omics data integration via deep graph convolutional networks for cancer subtype classification by Jiecheng Wu, Zhaoliang Chen, Shunxin Xiao, Genggeng Liu, Wenjie Wu, Shiping Wang

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Optimized Demand Forecasting for Bike-Sharing Stations Through Multi-Method Fusion and Gated Graph Convolutional Neural Networks by Hebin Guo, Kexin Li, Yutong Rou

Subjects: “…Gated graph convolutional neural network…”
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AD-GCN: A novel graph convolutional network integrating multi-omics data for enhanced Alzheimer’s disease diagnosis by Zilu Li, Qingyun Li, Xiaoqing Li, Wei Luo, Haiyan Guo, Chunyan Zhao, Canzhen Yang, Anke Xie, Kai Hu, Yangfan Guo

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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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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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Rolling Bearing Fault Diagnosis via Temporal-Graph Convolutional Fusion by Fan Li, Yunfeng Li, Dongfeng Wang

“…These were then input into a Temporal Convolutional Network (TCN) to capture multi-scale temporal dependencies. …”
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Lightweight pose estimation spatial-temporal enhanced graph convolutional model for miner behavior recognition by WANG Jianfang, DUAN Siyuan, PAN Hongguang, JING Ningbo

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SAGCN: Self-Attention Graph Convolutional Network for Human Pose Embedding by Zhongxiong Xu, Jiajun Hong, Yicong Yu, Chengzhu Lin, Linfei Yu, Meixian Xu

“…To address these limitations, we present SAGCN, a novel model integrating graph convolutional network (GCN) with self-attention. …”
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Exploring the Latent Information in Spatial Transcriptomics Data via Multi‐View Graph Convolutional Network Based on Implicit Contrastive Learning by Sheng Ren, Xingyu Liao, Farong Liu, Jie Li, Xin Gao, Bin Yu

“…This study introduces STMIGCL, a novel framework that leverages a multi‐view graph convolutional network and implicit contrastive learning. …”
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FreqMGCN-Net: An IoT-Integrated Multi-Parallel Graph Convolutional Network with frequency attention for motor bearing fault diagnosis by Jicai Wang, Shahrum Abdullah, Chen Gao, Azli Arifin, Salvinder Singh Karam Sing

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Novel similarity calculation method of multisource ontology based on graph convolution network by Liuqian SUN, Yuliang WEI, Bailing WANG

“…In the information age, the amount of data is growing exponentially.However, different data sources are heterogeneous, which makes it inconvenient to share and multiplex data.With the rapid development of semantic network, ontology mapping is an effective method to solve this problem.The core of ontology mapping is ontology similarity calculation.Therefore, a calculation method based on graph convolution network was proposed.Firstly, ontologiesare modeled as a heterogeneous graph network, then the graph convolution network was used to learn the text embedding rules, which made ontologies were definedin global unified representation.Lastly, multisource data fusion was completed.The experimental results show that the accuracy of the proposed method is higher than other methods, and the accuracy of multi-source data fusion was effectively improved.…”
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TGNet: tensor-based graph convolutional networks for multimodal brain network analysis by Zhaoming Kong, Rong Zhou, Xinwei Luo, Songlin Zhao, Ann B. Ragin, Alex D. Leow, Lifang He

“…In this paper, we propose a novel tensor-based graph convolutional network (TGNet) framework that combines tensor decomposition with multi-layer GCNs to capture both the homogeneity and intricate graph structures of multimodal brain networks. …”
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Semantics-Assisted Training Graph Convolution Network for Skeleton-Based Action Recognition by Huangshui Hu, Yu Cao, Yue Fang, Zhiqiang Meng

“…The fused features are then passed through three graph convolution blocks before being fed into fully connected layers for classification. …”
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A social recommendation model based on adaptive residual graph convolution networks by Rui Chen, Kangning Pang, Qingfang Liu, Lei Zhang, Hao Wu, Cundong Tang, Pu Li

“…To address the above problems, we propose a social recommendation model based on adaptive residual graph convolutional networks (SocialGCNRI). Specifically, we use the idea of fast Fourier transform (FFT), a filtering algorithm in the field of signal processing, to attenuate the raw data noise in the frequency domain, followed by utilizing the user-social relations, item-association relations, and user-item-interaction relations to form a heterogeneous graph to supplement the model information, and finally using a graph convolution algorithm with an adaptive residual graph to improve the expressive power of the model. …”
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