iPiDA-LGE: a local and global graph ensemble learning framework for identifying piRNA-disease associations by Hang Wei, Jialu Hou, Yumeng Liu, Alexey K. Shaytan, Bin Liu, Hao Wu

“…Results In this study, we propose a novel computational method called iPiDA-LGE for piRNA-disease association identification. iPiDA-LGE comprises two graph convolutional neural network modules based on local and global piRNA-disease graphs, aimed at capturing specific and general features of piRNA-disease pairs. …”
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Graph Feature Fusion-Driven Fault Diagnosis of Complex Process Industrial System Based on Multivariate Heterogeneous Data by Fengyuan Zhang, Jie Liu, Xiang Lu, Tao Li, Yi Li, Yongji Sheng, Hu Wang, Yingwei Liu

“…Second, the node features and system spatial features of the subgraphs are extracted by the graph convolutional neural network at the same time, and the fault representation information of the subgraph is mined. …”
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Knowledge Graph–Enhanced Deep Learning Model (H-SYSTEM) for Hypertensive Intracerebral Hemorrhage: Model Development and Validation by Yulong Xia, Jie Li, Bo Deng, Qilin Huang, Fenglin Cai, Yanfeng Xie, Xiaochuan Sun, Quanhong Shi, Wei Dan, Yan Zhan, Li Jiang

“…The bidirectional encoder representations from transformers, inflated dilated convolutional neural network, bidirectional long short-term memory, and conditional random fields (BERT-IDCNN-BiLSTM-CRF) model was used as the key NER module of the H-SYSTEM due to its fast convergence and efficient extraction of key named entities, achieved the highest performance among 7 key NER models (precision=92.03, recall=90.22, and F1PPP ConclusionsThe H-SYSTEM showed significantly high efficiency and generalization capacity in processing electronic medical records, and it provided explainable and elaborate treatment plans. …”
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I-AIR: intention-aware travel itinerary recommendation via multi-signal fusion and spatiotemporal constraints by Xiao Cui, Zhihua Wang, Ping Li, Qiang Xu

“…The model combines a multi-head self-attention transformer to capture the sequential and temporal dynamics of user behavior, with a graph convolutional network (GCN) that models complex co-visitation patterns among POIs. …”
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Flexible integration of spatial and expression information for precise spot embedding via ZINB-based graph-enhanced autoencoder by Jiacheng Leng, Jiating Yu, Ling-Yun Wu, Hongyang Chen

“…To address these issues, we introduce Spot2vector, a computational framework that leverages a graph-enhanced autoencoder integrating zero-inflated negative binomial distribution modeling, combining both graph convolutional networks and graph attention networks to extract the latent embeddings of spots. …”
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PRDAGE: a prescription recommendation framework for traditional Chinese medicine based on data augmentation and multi-graph embedding by Zhihua Wen, Yunchun Dong, Lihong Peng, Longxin Zhang, Junfeng Yan

“…Additionally, we developed a multi-layer embedding method for symptoms and herbs, using Sentence Bert (SBert) and graph convolutional networks. The aim of this multi-layer embedding method is to capture and represent the semantic information of symptoms and herbs, as well as the complex relationships between 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

“…Utilizing teaching plans, syllabi, and approximately 500,000 words of course materials from 17 courses, we first extracted 500 knowledge points via the Term Frequency–Inverse Document Frequency (TF-IDF) algorithm to build a baseline course–knowledge matrix and visualize the preliminary graph using Graph Convolutional Networks (GCN) and Neo4j. …”
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Multimodal Deep Learning for Android Malware Classification by James Arrowsmith, Teo Susnjak, Julian Jang-Jaccard

“…We synthesise these modalities by combining predictions from convolutional and graph neural networks with a multilayer perceptron. …”
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Intelligent data-driven system for mold manufacturing using reinforcement learning and knowledge graph personalized optimization for customized production by Chengcai He, Jiaxing Deng, Jingchun Wu, Beicheng Qin, Jinxiang Chen, Yan Li, Qiangsheng Huang

“…The experimental results reveal two key findings: (1) Within the enhanced learning knowledge graph framework, the algorithm—optimized using a graph convolutional network—achieves consistently higher qualification rates across test samples. …”
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Predicting noncoding RNA and disease associations using multigraph contrastive learning by Si-Lin Sun, Yue-Yi Jiang, Jun-Ping Yang, Yu-Han Xiu, Anas Bilal, Hai-Xia Long

“…The third step is to use an encoder with a Graph Convolutional Network (GCN) architecture to extract embedding vectors. …”
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Intelligent prediction method of virtual network function resource capacity for polymorphic network service slicing by Julong LAN, Di ZHU, Dan LI

“…For the extraction of spatial features, by given an adjacency matrix and a feature matrix,graph convolutional network is used to reorganize the spatial distribution features of time series in the Fourier domain. …”
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MultiFG: integrating molecular fingerprints and graph embeddings via attention mechanisms for robust drug side effect prediction by Zuhai Hu, Jinxiang Yang, Linghao Ni, Liyuan Zhang, Bin Peng

“…MultiFG incorporates attention-enhanced convolutional networks and utilizes the recently developed Kolmogorov-Arnold Networks (KAN) as the prediction layer to effectively capture complex relationships. …”
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Vessel Type Recognition Using a Multi-Graph Fusion Method Integrating Vessel Trajectory Sequence and Dependency Relations by Lin Ye, Xiaohui Chen, Haiyan Liu, Ran Zhang, Bing Zhang, Yunpeng Zhao, Dewei Zhou

“…These graph structures are then processed through graph convolutional networks (GCNs), which integrate various sources of information within the graphs to obtain behavioral representations of vessel trajectories. …”
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Corrosion resistance prediction of high-entropy alloys: framework and knowledge graph-driven method integrating composition, processing, and crystal structure by Guangxuan Song, Dongmei Fu, Yongjie Lin, Lingwei Ma, Dawei Zhang

“…A deep learning model, Mat-NRKG, is developed based on the CPSP framework, efficiently integrating composition, processing, and crystal structure data through a knowledge graph and graph convolutional network. Evaluations using the HEA-CRD dataset show that the CPSP Framework outperforms the Composition-Only Prediction Framework (CP Framework) and the Composition and Processing-Based Prediction Framework (CPP Framework). …”
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Feature-enriched hyperbolic network geometry by Roya Aliakbarisani, M. Ángeles Serrano, Marián Boguñá

“…Notably, node features are at the core of deep learning techniques, such as graph convolutional neural networks (GCNs), offering great utility in downstream tasks. …”
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Design of an improved graph-based model for real-time anomaly detection in healthcare using hybrid CNN-LSTM and federated learning by G Muni Nagamani, Chanumolu Kiran Kumar

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Cross Attentive Multi-Cue Fusion for Skeleton-Based Sign Language Recognition by Ogulcan Ozdemir, Inci M. Baytas, Lale Akarun

“…We demonstrate how the proposed attention-based framework exposes distinct temporal patterns of visual cue representations extracted via Spatio-Temporal Graph Convolutional Network (ST-GCN) and exploits them for learning SL representations more effectively. …”
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An Anomaly Detection Method for Industrial System Cybersecurity Based on GGL-WAVE-CNN by Bing Zou, Ke jun Zhang, Xin Ying Yu, Yu han Jin, Jun Wang, Ling yu Liu

“…This paper introduces a novel two-level anomaly detection framework that combines the generalized graph Laplacian (GGL), wavelet decomposition (WAVE), and an enhanced convolutional neural network (CNN). …”
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An Efficient Recommendation Algorithm Based on Heterogeneous Information Network by Ying Yin, Wanning Zheng

“…Therefore, this paper proposes an efficient recommendation algorithm based on heterogeneous information network, which uses the characteristics of graph convolution neural network to automatically learn node information to extract heterogeneous information and avoid errors caused by the manual search for metapaths. …”
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Approaches to Proxy Modeling of Gas Reservoirs by Alexander Perepelkin, Anar Sharifov, Daniil Titov, Zakhar Shandrygolov, Denis Derkach, Shamil Islamov

“…The methodology integrates graph neural networks to account for spatial interdependencies between wells with recurrent and convolutional neural networks for time-series analysis. …”
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