Multiscale Graph Transformer Network With Dynamic Superpixel Pyramid for Hyperspectral Image Classification by Tingting Wang, Yao Sun, Yunfeng Hu

“…To address these limitations, we propose a multi-scale graph transformer network (MSGTN), which captures spatial features at different scales through multiscale graph convolutional networks (GCNs) with adaptive graph structures. …”
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Objects outline delineation by nonliniear filtration methods for boundary pixels by D. V. Zaerko, V. A. Lipnitski, N. L. Bobrova, Dm. V. Zaerko

“…This problem is actual also in other algorithms, which have a common feature – 2D convolution operation. The level of importance are defined by multi used nonlinear filters to border pixels and then union a few images without distortions at the boundaries. …”
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MSVMD-Informer: A Multi-Variate Multi-Scale Method to Wind Power Prediction by Zhijian Liu, Jikai Chen, Hang Dong, Zizhuo Wang

“…Adaptive graph convolution is applied to extract correlations between scales, while self-attention mechanisms are utilized to capture temporal dependencies within the same scale. …”
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MultiSenseNet: Multi-Modal Deep Learning for Machine Failure Risk Prediction by Mostafijur Rahman, Md Sabbir Hossain, Uland Rozario, Satyabrata Roy, M. F. Mridha, Nilanjan Dey

“…This paper presents a method for predicting equipment failures using multi-modal sensor data. Their approach combines advanced techniques, including convolutional neural networks (CNNs) for feature extraction, long short-term memory networks (LSTMs) for temporal patterns, transformer-based attention mechanisms for critical feature identification, and graph neural networks (GNNs) for modeling sensor-machine relationships. …”
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Spatial-Similarity Dynamic Graph Bidirectional Double-Cell Network for Traffic Flow Prediction by Zhifei Yang, Zeyang Li, Jia Zhang

“…The proposed architecture incorporates two innovative components: 1) a Spatial Similarity Dynamic Graph Convolution (SDGCN) module that adaptively aggregates spatial features through node similarity analysis and time-varying graph structures, and 2) a Bidirectional Double-Cell Recurrent Neural Network (Bi-DouCRNN) that combines LSTM and GRU mechanisms via dual-gating operations to capture multi-scale temporal dynamics. …”
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GNODEVAE: a graph-based ODE-VAE enhances clustering for single-cell data by Zeyu Fu, Chunlin Chen, Song Wang, Junping Wang, Shilei Chen

“…Through systematic evaluation across 10 graph convolutional layers, GAT demonstrated optimal performance, achieving average ARI advantages of 0.108 and 0.112 over alternative graph convolutional layers in VGAE and GNODEVAE architectures respectively, along with ASW advantages of 0.047 and 0.098. …”
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Expanding and Interpreting Financial Statement Fraud Detection Using Supply Chain Knowledge Graphs by Shanshan Zhu, Tengyun Ma, Haotian Wu, Jifan Ren, Daojing He, Yubin Li, Rui Ge

“…To address these gaps, this paper introduces an interpretable and efficient Heterogeneous Graph Convolutional Network (ieHGCN) designed to analyze supply chain knowledge graphs. …”
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Dynamic Spatial–Temporal Graph Neural Network for Cooling Capacity Prediction in HVDC Systems by Hao Sun, Shaosen Li, Jianxiang Huang, Hao Li, Guanxin Jing, Ye Tao, Xincui Tian

“…This task involves handling complex, multi-dimensional time-series data with strong inter-variable dependencies and temporal dynamics. …”
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Multi-fusion strategy network-guided cancer subtypes discovering based on multi-omics data by Jian Liu, Xinzheng Xue, Pengbo Wen, Qian Song, Jun Yao, Shuguang Ge

“…SMMSN can not only fuse multi-level data representations of single omics data by Graph Convolutional Network (GCN) and Stacked Autoencoder Network (SAE), but also achieve the organic fusion of multi- -omics data through multiple fusion strategies. …”
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Relationship extraction between entities with long distance dependencies and noise based on semantic and syntactic features by Lei Wang, Fei Wu, Xiaoqing Liu, Jilong Cao, Mingwei Ma, Zhaoyang Qu

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EKNet: Graph Structure Feature Extraction and Registration for Collaborative 3D Reconstruction in Architectural Scenes by Changyu Qian, Hanqiang Deng, Xiangrong Ni, Dong Wang, Bangqi Wei, Hao Chen, Jian Huang

“…Experiments demonstrate that the proposed method achieves a 27.28% improvement in registration speed compared to traditional GCN (Graph Convolutional Neural Networks) and an 80.66% increase in registration accuracy over the suboptimal method. …”
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Spectro-Image Analysis with Vision Graph Neural Networks and Contrastive Learning for Parkinson’s Disease Detection by Nuwan Madusanka, Hadi Sedigh Malekroodi, H. M. K. K. M. B. Herath, Chaminda Hewage, Myunggi Yi, Byeong-Il Lee

“…The integration of graph neural networks with contrastive learning enables effective learning from limited labeled data while capturing complex spectro-temporal relationships that traditional Convolution Neural Network (CNN) approaches miss, representing a promising direction for developing more accurate and clinically viable speech-based diagnostic tools for PD.…”
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Graph-Enhanced Deep Learning With Character Similarity Mining for Automated NOTAM Correction in Aviation Systems by Bing Dong, Haoran Yao, Chuang Luo, Ruichao Yang, Ziyue Wang

“…To address this challenge, we used raw NOTAM data from the Civil Aviation Information Center, from September 2021 to September 2023. A trained relational graph convolutional neural network (CEV-RGCN) model, along with a tianzege-convolutional neural network (CNN), were employed to calculate the feature similarity of Chinese characters based on phonetics and glyphs. …”
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The analysis of artificial intelligence knowledge graphs for online music learning platform under deep learning by Shen Jiang, Ningning Shi, Chang Liu

“…Abstract This work proposes a personalized music learning platform model based on deep learning, aiming to provide efficient and customized learning recommendations by integrating audio, video, and user behavior data. This work uses Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) networks to extract audio and video features, while using multi-layer perceptrons to encode user behavior data. …”
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DaGAM-Trans: Dual graph attention module-based transformer for offline signature forgery detection by Sara Tehsin, Ali Hassan, Farhan Riaz, Inzamam Mashood Nasir

“…The architecture comprises a Graph Attention Module (GAM) to capture spatial dependencies using multi-head graph attention and graph convolution layers, and a Channel Attention Module (CAM) to amplify discriminative features and suppress irrelevant information. …”
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Advancing ADMET prediction for major CYP450 isoforms: graph-based models, limitations, and future directions by Asmaa A. Abdelwahab, Mustafa A. Elattar, Sahar Ali Fawzi

“…This review provides a comprehensive exploration of how graph-based computational techniques, including Graph Neural Networks (GNNs), Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs), have emerged as powerful tools for modeling complex CYP enzyme interactions and predicting ADMET properties with improved precision. …”
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A Lightweight Semantic- and Graph-Guided Network for Advanced Optical Remote Sensing Image Salient Object Detection by Jie Liu, Jinpeng He, Huaixin Chen, Ruoyu Yang, Ying Huang

“…This module incorporates non-local operations under graph convolution domain to deeply explore high-order relationships between adjacent layers, while utilizing depth-wise separable convolution blocks to significantly reduce computational cost. …”
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A Multiscale Mixed-Graph Neural Network Based on Kinematic and Dynamic Joint Features for Human Motion Prediction by Rongyong Zhao, Bingyu Wei, Lingchen Han, Yuxin Cai, Yunlong Ma, Cuiling Li

“…The extraction of joint features at each scale is facilitated by a single-scale mixed-graph convolution module. And to effectively integrate the extracted kinematic and dynamic features, a KD-fused Graph-GRU (Kinematic and Dynamics Fused Graph Gate Recurrent Unit) predictor is designed to fuse them. …”
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A KeyBERT-Enhanced Pipeline for Electronic Information Curriculum Knowledge Graphs: Design, Evaluation, and Ontology Alignment by Guanghe Zhuang, Xiang Lu

“…Electronic Information Engineering curricula encompass diverse and rapidly evolving topics; however, existing knowledge graphs often overlook multi-word concepts and more nuanced semantic relationships. …”
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CGD-CD: A Contrastive Learning-Guided Graph Diffusion Model for Change Detection in Remote Sensing Images by Yang Shang, Zicheng Lei, Keming Chen, Qianqian Li, Xinyu Zhao

“…With the rapid development of remote sensing technology, the question of how to leverage large amounts of unlabeled remote sensing data to detect changes in multi-temporal images has become a significant challenge. …”
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