Rotation Invariance in Graph Convolutional Networks by Nguyen Anh Mac, Hung Son Nguyen

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

Subjects: “…graph convolutional network…”
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Feature recommendation strategy for graph convolutional network by Jisheng Qin, Xiaoqin Zeng, Shengli Wu, Yang Zou

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Structural reinforcement-based graph convolutional networks by Jisheng Qin, Qianqian Wang, Tao Tao

Subjects: “…graph convolutional network…”
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Graph convolution network for fraud detection in bitcoin transactions by Ahmad Asiri, K. Somasundaram

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Augmented Graph Convolutional Network for Enhancing Label Reachability by Xiangyi Wang, Fengjun Zhang, Wei Teng, Baoda Liu

Subjects: “…Graph convolutional networks…”
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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

“…Thus, the fusion of Convolutional Neural Networks with Recurrent Neural Networks and Graph Networks enhances the efficiency of the detection process.…”
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A novel Knowledge Graph recommendation algorithm based on Graph Convolutional Network by Hui Guo, Chengyong Yang, Liqing Zhou, Shiwei Wei

Subjects: “…Graph Convolutional Network…”
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ECG-GraphNet: Advanced arrhythmia classification based on graph convolutional networks by Myeonghun Lee, BS, Jiwoo Lim, MS, JinKook Kim, MS

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Transductive zero-shot learning via knowledge graph and graph convolutional networks by Qiong Li, Xin Sun, Junyu Dong

“…To tackle this problem, we propose a transductive zero-shot learning method, based on Knowledge Graph and Graph Convolutional Network. We firstly learn a knowledge graph, where each node represents a category encoded by its semantic embedding. …”
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CSC-GCN: Contrastive semantic calibration for graph convolution network by Xu Yang, Kun Wei, Cheng Deng

Subjects: “…Graph convolution 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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An adaptive spatiotemporal dynamic graph convolutional network for traffic prediction by Zhiguo Xiao, Qi Shen, Changgen Li, Dongni Li, Qian Liu

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Identifying T cell antigen at the atomic level with graph convolutional network by Jinhao Que, Guangfu Xue, Tao Wang, Xiyun Jin, Zuxiang Wang, Yideng Cai, Wenyi Yang, Meng Luo, Qian Ding, Jinwei Zhang, Yilin Wang, Yuexin Yang, Fenglan Pang, Yi Hui, Zheng Wei, Jun Xiong, Shouping Xu, Yi Lin, Haoxiu Sun, Pingping Wang, Zhaochun Xu, Qinghua Jiang

“…Here we propose deepAntigen, a graph convolutional network-based framework, to identify T cell antigens at the atomic level. deepAntigen achieves excellent performance both in the prediction of antigen-human leukocyte antigen (HLA) binding and antigen-T cell receptor (TCR) interactions, which can provide comprehensive guidance for identification of T cell antigens. …”
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Knowledge graph convolutional networks with user preferences for course recommendation by Zhong Hua, Jianbai Yang, Weidong Ji

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Contrastive Learning‑based Simplified Graph Convolutional Network Recommendation by YU Yuchen, WU Siqi, ZHAO Qinghua, WU Xuhong, WANG Lei

Subjects: “…graph convolutional network…”
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Application of improved graph convolutional network for cortical surface parcellation by Jia Tan, Xiaomei Ren, Yong Chen, Xianju Yuan, Feiba Chang, Rui Yang, Chengqun Ma, Xiaoyu Chen, Miao Tian, Wei Chen, Zihong Wang

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Combining Long-Term Recurrent Convolutional and Graph Convolutional Networks to Detect Phishing Sites Using URL and HTML by Subhash Ariyadasa, Shantha Fernando, Subha Fernando

“…This paper proposes PhishDet, a new way of detecting phishing websites through Long-term Recurrent Convolutional Network and Graph Convolutional Network using URL and HTML features. …”
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Integrating Graph Convolution and Attention Mechanism for Kinase Inhibition Prediction by Hamza Zahid, Kil To Chong, Hilal Tayara

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Multiscale network alignment model based on convolution of homogeneous multilayer graphs by CUI Jiahao, JIANG Tao, XU Mengyao

“…In terms of node characteristics, the K-nearest neighbor algorithm was used to aggregate node neighborhood information to model the deep network structure, so as to enhance the data. In terms of graph convolution, the convolution process was guided by the construction of a homogeneity matrix according to the network homogeneity, and the social networks of different scales were processed based on the network community structure. …”
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