Fault diagnosis of power transformers based on dissolved gas analysis and multi-kernel graph convolution network integrated with dual-channel classifiers by Xuebin Lv, Fuzheng Liu, Mingshun Jiang, Faye Zhang, Lei Jia

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Prediction of the Association between Transfer RNA and Diseases: A Deep Learning Approach Combining Multi-View Graph Convolution and Attention Mechanisms by Xiangkui Li, Dengju Yao, Bo Zhang, Xiaojuan Zhan, Jian Zhang

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LDAGM: prediction lncRNA-disease asociations by graph convolutional auto-encoder and multilayer perceptron based on multi-view heterogeneous networks by Bing Zhang, Haoyu Wang, Chao Ma, Hai Huang, Zhou Fang, Jiaxing Qu

“…Next, by combining the obtained deep topological features with the similarity network of lncRNA, disease, and miRNA interactions, we construct a multi-view heterogeneous network model. The Graph Convolutional Autoencoder is employed for nonlinear feature extraction. …”
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An Adaptive Spatio-Temporal Traffic Flow Prediction Using Self-Attention and Multi-Graph Networks by Basma Alsehaimi, Ohoud Alzamzami, Nahed Alowidi, Manar Ali

“…The ASTAM employs multi-temporal gated convolution with multi-scale temporal input segments to model complex non-linear temporal correlations. …”
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Emoji-Driven Sentiment Analysis for Social Bot Detection with Relational Graph Convolutional Networks by Kaqian Zeng, Zhao Li, Xiujuan Wang

“…Finally, a Relational Graph Convolutional Network (RGCN) is employed to model heterogeneous social topology for robust bot detection. …”
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Cross-Scale Spatial Refinement Graph Convolutional Network for Skeleton-Based Action Recognition by Chengyuan Ke, Sheng Liu, Zhenghao Ke, Yuan Feng, Shengyong Chen

“…The AGP module uses graph pooling to construct multi-scale skeletal sub-graphs, capturing implicit joint relationships and preserving crucial motion details. …”
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Enhanced Attention-Driven Dynamic Graph Convolutional Network for Extracting Drug-Drug Interaction by Xiechao Guo, Dandan Song, Fang Yang

“…Our model combines the Attention-driven Dynamic Graph Convolutional Network (ADGCN) with a feature fusion method and multi-task learning framework. …”
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GMFLDA: Improved Prediction of lncRNA-Disease Association via Graph Convolutional Network by Kwangsu Kim, Jihwan Ha

“…In this study, we present GMFLDA, an advanced machine learning framework for inferring lncRNA-disease associations (LDA) by synergizing graph convolutional networks (GCNs) with deep matrix factorization. …”
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Graph convolutional network as a fast statistical emulator for numerical ice sheet modeling by Younghyun Koo, Maryam Rahnemoonfar

“…When applied to transient simulations of the Pine Island Glacier (PIG), Antarctica, the GCN successfully reproduces ice thickness and velocity with a correlation coefficient of approximately 0.997, outperforming non-graph models, including fully convolutional network (FCN) and multi-layer perceptron (MLP). …”
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Secondary Operation Risk Assessment Method Integrating Graph Convolutional Networks and Semantic Embeddings by Pengyu Zhu, Youwei Li, Peidong Xu, Ping Li, Zhenbing Zhao, Gang Li

“…To address this issue, this paper proposes a hybrid model that integrates graph convolutional networks (GCNs) with semantic embedding techniques. …”
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Sign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks by Pathomthat Chiradeja, Yijuan Liang, Chaiyan Jettanasen

“…This study introduces an innovative sign language sentence recognition (SLSR) approach using Hybrid Graph Embedding and Adaptive Convolutional Networks (HGE-ACN) specifically developed for single-handed wearable glove devices. …”
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Lightweight Multiscale Spatio-Temporal Graph Convolutional Network for Skeleton-Based Action Recognition by Zhiyun Zheng, Qilong Yuan, Huaizhu Zhang, Yizhou Wang, Junfeng Wang

“…Using skeletal information to model and recognize human actions is currently a hot research subject in the realm of Human Action Recognition (HAR). Graph Convolutional Networks (GCN) have gained popularity in this discipline due to their capacity to efficiently process graph-structured data. …”
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Prediction of reproductive and developmental toxicity using an attention and gate augmented graph convolutional network by Si Hoon Lee, Eunwoo Choi, JunHo Park, Seohwi Yoon, Myung-Ha Song, Ji Young Lee, Jungkwan Seo, Sun Kyung Shin, Sang Hee Lee, Han Bin Oh

“…In this study, we developed a descriptor-free deep learning model by constructing a Graph Convolutional Network designed with multi-head attention and gated skip-connections to predict reproductive and developmental toxicity. …”
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Aspect-Based Sentiment Analysis Through Graph Convolutional Networks and Joint Task Learning by Hongyu Han, Shengjie Wang, Baojun Qiao, Lanxue Dang, Xiaomei Zou, Hui Xue, Yingqi Wang

“…The proposed model utilizes dependency trees combined with self-attention mechanisms to generate new weight matrices, emphasizing the locational information of aspect terms, and optimizes the graph convolutional network (GCN) to extract aspect terms more efficiently. …”
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Research on multi dimensional feature extraction and recognition of industrial and mining solid waste images based on mask R-CNN and graph convolutional networks by Shuqin Wang, Na Cheng, Yan Hu

“…Abstract Aiming at the problems of traditional methods for multi-dimensional feature extraction of industrial and mining solid waste images, such as single feature extraction, difficult fusion, missing high-order features, weak generalization ability and low computational efficiency, an innovative solution combining Mask R-CNN with Graph Convolutional Networks (GCN) was proposed to achieve automatic, multi-dimensional and efficient feature extraction. …”
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Wind Power Forecasting Based on Multi-Graph Neural Networks Considering External Disturbances by Xiaoyin Xu, Zhumei Luo, Menglong Feng

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Multi-component attention graph convolutional neural network for QoS-aware cloud job scheduling and resource management enhancing efficiency and performance in cloud computing by Deepak Dharrao, Sarika Deokate, Nagamma Hudgi, Shrinivas Shirkande, Vinod Mahajan, Madhuri Pangavhane, Anupkumar Bongale

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STFDSGCN: Spatio-Temporal Fusion Graph Neural Network Based on Dynamic Sparse Graph Convolution GRU for Traffic Flow Forecast by Jiahao Chang, Jiali Yin, Yanrong Hao, Chengxin Gao

“…The dynamic sparse graph convolution gated recurrent unit (DSGCN-GRU) in this model is a novel component that integrates adaptive dynamic sparse graph convolution into the gated recurrent network to simulate the diffusion of information within a dynamic spatial structure. …”
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Enhanced Wind Power Forecasting Using Graph Convolutional Networks with Ramp Characterization and Error Correction by Xin He, Yichen Ma, Jiancang Xie, Gang Zhang, Tuo Xie

“…This study proposes a wind power prediction approach based on graph convolutional networks, incorporating ramp feature recognition and error correction mechanisms. …”
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Node-Based Graph Convolutional Network With SLIC Method for Breast Cancer Ultrasound Images Classification by Kien Trang, Fung Fung Ting, Bao Quoc Vuong, Chee-Ming Ting

“…This research presents a novel node-based Graph Convolutional Network (GCN) approach for the classification of breast cancer from ultrasound images. …”
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