Multi-Semantic Alignment Graph Convolutional Network by Jisheng Qin, Xiaoqin Zeng, Shengli Wu, Yang Zou

Subjects: “…graph convolutional network…”
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Multi-channel based edge-learning graph convolutional network by Shuai YANG, Ruiqin WANG, Hui MA

Subjects: “…graph convolutional network…”
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Multi-Head Spatiotemporal Attention Graph Convolutional Network for Traffic Prediction by Ariyo Oluwasanmi, Muhammad Umar Aftab, Zhiguang Qin, Muhammad Shahzad Sarfraz, Yang Yu, Hafiz Tayyab Rauf

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SMS spam detection using BERT and multi-graph convolutional networks by Linjie Shen, Yanbin Wang, Zhao Li, Wenrui Ma

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Multi-Step Parking Demand Prediction Model Based on Multi-Graph Convolutional Transformer by Yixiong Zhou, Xiaofei Ye, Xingchen Yan, Tao Wang, Jun Chen

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AFMF: adaptive fusion of multi-hop neighborhood features in graph convolutional network by Kang Liu, Xinyu Li, Lian Liu, Zhihao Xv, Aohang Pei, Runshi Ji

“…To address this issue, we propose a graph convolutional network based on Adaptive Fusion of Multi-hop Features, termed AFMF, which can adaptively generate feature fusion weights according to the features of multi-hop nodes, solving the over-smoothing problem caused by shared fusion weights. …”
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MulGCN: MultiGraph Convolutional Network for Aspect-Level Sentiment Analysis by Huyen Trang Phan, Van Du Nguyen, Ngoc Thanh Nguyen

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Multi dynamic temporal representation graph convolutional network for traffic flow prediction by Zuojun Wu, Xiaojun Liu, Xiaoling Zhang

“…To address this issue, we propose a novel Multi Dynamic Temporal Representation Graph Convolutional Network (MDTRGCN). …”
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Pedestrian Trajectory Prediction Based on Transformer and Multi-relation Graph Convolutional Networks by LIU Guihong, ZHOU Zongrun, MENG Xiangfu

“…To address this, a pedestrian trajectory prediction model combining Transformer and multi-relation graph convolutional network (GCN) is proposed. …”
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A Hybrid Model for Soybean Yield Prediction Integrating Convolutional Neural Networks, Recurrent Neural Networks, and Graph Convolutional Networks by Vikram S. Ingole, Ujwala A. Kshirsagar, Vikash Singh, Manish Varun Yadav, Bipin Krishna, Roshan Kumar

“…In this line, this work fuses multi-source data—satellite imagery, weather data, and soil properties—through the approach of multi-modal fusion using Convolutional Neural Networks and Recurrent Neural Networks. …”
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Multimodal depression detection based on an attention graph convolution and transformer by Xiaowen Jia, Jingxia Chen, Kexin Liu, Qian Wang, Jialing He

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Multi-featured spatial-temporal and dynamic multi-graph convolutional network for metro passenger flow prediction by Chuan Zhao, Xin Li, Zezhi Shao, HongJi Yang, Fei Wang

“…To address these challenges, we developed a novel model called the multi-featured spatial-temporal (MFST) and dynamic multi-graph convolutional network (DMGCN) model. …”
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Multiscale network alignment model based on convolution of homogeneous multilayer graphs by CUI Jiahao, JIANG Tao, XU Mengyao

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Cross-device fault diagnosis method based on graph convolution and multi-sensor fusion by SUN Yuanshuai, KONG Fanqin, NIE Xiaoyin, XIE Gang

Subjects: “…Graph convolutional neural network…”
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MO-GCN: A multi-omics graph convolutional network for discriminative analysis of schizophrenia by Haiyuan Wang, Runlin Peng, Yuanyuan Huang, Liqin Liang, Wei Wang, Baoyuan Zhu, Chenyang Gao, Minxin Guo, Jing Zhou, Hehua Li, Xiaobo Li, Yuping Ning, Fengchun Wu, Kai Wu

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Land-sea Clutter Classification Method Based on Multi-channel Graph Convolutional Networks by Can LI, Zengfu WANG, Xiaoxuan ZHANG, Quan PAN

“…We propose a Multi-Channel Graph Convolutional Networks (MC-GCN) for land-sea clutter classification. …”
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Multi-stream part-fused graph convolutional networks for skeleton-based gait recognition by Likai Wang, Jinyan Chen, Zhenghang Chen, Yuxin Liu, Haolin Yang

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The node importance evaluation method based on graph convolution in multilayer heterogeneous networks by Zhixing Chen, Jian Shu, Linlan Liu

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EEG Emotion Recognition Using AttGraph: A Multi-Dimensional Attention-Based Dynamic Graph Convolutional Network by Shuai Zhang, Chengxi Chu, Xin Zhang, Xiu Zhang

“…Methods: To address these challenges, this paper proposes a multi-dimensional attention-based dynamic graph convolutional neural network (AttGraph) model. …”
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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

“…Therefore, we propose a multi-head self-attention spatiotemporal graph convolutional network (MSASGCN) model. …”
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