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

“…Abstract Background Exploring piRNA-disease associations can help discover candidate diagnostic or prognostic biomarkers and therapeutic targets. …”
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Combination of Graph and Convolutional Networks for Brain Tumor Segmentation from Multi-Modal MR Images In Clinical Applications by Marjan Vatanpour, Javad Haddadnia, Shahryar Salmani Bajestani

“…After retrieving the feature representation of the CNN, a graph model is created and fed to the GNN. The CNN will help to capture local information of patches and GNN will retrieve the global information available in the data which together can provide promising results. …”
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MSA-GCN: Exploiting Multi-Scale Temporal Dynamics With Adaptive Graph Convolution for Skeleton-Based Action Recognition by Kowovi Comivi Alowonou, Ji-Hyeong Han

“…An adaptive graph built upon the global context information of the joints can help move beyond this limitation. …”
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AI modeling for outbreak prediction: A graph-neural-network approach for identifying vancomycin-resistant enterococcus carriers. by Gregor Donabauer, Anca Rath, Aila Caplunik-Pratsch, Anja Eichner, Jürgen Fritsch, Martin Kieninger, Susanne Gaube, Wulf Schneider-Brachert, Udo Kruschwitz, Bärbel Kieninger

“…We used data from 8,372 patients, combining more than 125,000 movements within our hospital with patient-related information to create time-dependent graph sequences and applied graph neural networks (GNNs) to classify patients as VRE carriers or noncarriers. …”
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An automated construction method of 3D knowledge graph based on multi-agent systems in virtual geographic scene by Yukun Guo, Jun Zhu, Jianlin Wu, Jinbin Zhang, Zhihao Guo, Weilian Li, Heng Zhang

“…However, the multitude of object types and complex relationships they contain make it challenging to display embedded geographic knowledge. Knowledge graphs help organize object entities and their relationships within scenes, which makes it easier to store and express geographic knowledge. …”
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Integrating and retrieving learning analytics data from heterogeneous platforms using ontology alignment: Graph-based approach by Mohd Hafizan Musa, Sazilah Salam, Siti Feirusz Ahmad Fesol, Muhammad Syahmie Shabarudin, Jack Febrian Rusdi, Mohd Adili Norasikin, Ibrahim Ahmad

“…This study explores the possibility of integrating and retrieving heterogenous data across platforms by using ontology graph databases to enhance educational insights and enabling advanced data-driven decision-making. …”
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Research on the Response of Urban Sustainable Development Standards to the United Nations Sustainable Development Goals Based on Knowledge Graphs by Maomao Yan, Feng Yang, Huiyao Pan, Chao Li

“…In addition, the process of establishing and maintaining knowledge graphs facilitates the continuous adoption of new standards through which the indicator library is automatically updated. …”
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MCGS-ReID: A Visible-Infrared Vehicle Reidentification Method Using Modal-Cross Graph Sampler by Jianfei Liu, Chunhui Zhao, Chen Zhao, Nan Su, Wanxuan Lu, Yiming Yan, Shou Feng, Yunfei Qu

“…To alleviate these differences, this article proposes a cross-modal vehicle reidentification network based on modal-cross graph sampling (MCGS) method: first, a MCGS method is proposed, which can help the subsequent network learn more cross-modal information and reduce the modality differences. …”
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Predicting correlation relationships of entities between attack patterns and techniques based on word embedding and graph convolutional network by Weicheng QIU, Xiuzhen CHEN, Yinghua MA, Jin MA, Zhihong ZHOU

“…Threat analysis relies on knowledge bases that contain a large number of security entities.The scope and impact of security threats and risks are evaluated by modeling threat sources, attack capabilities, attack motivations, and threat paths, taking into consideration the vulnerability of assets in the system and the security measures implemented.However, the lack of entity relations between these knowledge bases hinders the security event tracking and attack path generation.To complement entity relations between CAPEC and ATT＆amp;CK techniques and enrich threat paths, an entity correlation prediction method called WGS was proposed, in which entity descriptions were analyzed based on word embedding and a graph convolution network.A Word2Vec model was trained in the proposed method for security domain to extract domain-specific semantic features and a GCN model to capture the co-occurrence between words and sentences in entity descriptions.The relationship between entities was predicted by a Siamese network that combines these two features.The inclusion of external semantic information helped address the few-shot learning problem caused by limited entity relations in the existing knowledge base.Additionally, dynamic negative sampling and regularization was applied in model training.Experiments conducted on CAPEC and ATT＆amp;CK database provided by MITRE demonstrate that WGS effectively separates related entity pairs from irrelevant ones in the sample space and accurately predicts new entity relations.The proposed method achieves higher prediction accuracy in few-shot learning and requires shorter training time and less computing resources compared to the Bert-based text similarity prediction models.It proves that word embedding and graph convolutional network based entity relation prediction method can extract new entity correlation relationships between attack patterns and techniques.This helps to abstract attack techniques and tactics from low-level vulnerabilities and weaknesses in security threat analysis.…”
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Semantic Fusion of Health Data: Implementing a Federated Virtualized Knowledge Graph Framework Leveraging Ontop System by Abid Ali Fareedi, Stephane Gagnon, Ahmad Ghazawneh, Raul Valverde

“…The ODSRE constructs a systematic mechanism leveraging the Ontop virtual paradigm to establish a state-of-the-art federated virtual knowledge graph framework (FVKG) embedded virtualized knowledge graph approach to mitigate the aforementioned challenges effectively. …”
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Forecasting Groundwater Level by Characterizing Multiple Spatial Dependencies of Environmental Factors Using Graph‐Based Deep Learning by Yinghan Wu, Gang Mei, Kaixuan Shao, Nengxiong Xu, Jianbing Peng

“…In this study, we characterize spatial dependencies of groundwater from multiple perspectives and investigate the impact on the forecasting results of groundwater dynamics using graph‐based deep learning. Characterizing spatial dependencies helps improve the understanding of groundwater dynamics, but its effectiveness in enhancing prediction accuracy depends on the characteristics of spatial interactions. …”
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Latent Graph Attention for Spatial Context in Light-Weight Networks: Multi-Domain Applications in Visual Perception Tasks by Ayush Singh, Yash Bhambhu, Himanshu Buckchash, Deepak K. Gupta, Dilip K. Prasad

“…Conventional attention-based and graph-based models capture the global context to a large extent; however, these are computationally expensive. …”
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Stable Variable Fixation for Accelerated Unit Commitment via Graph Neural Network and Linear Programming Hybrid Learning by Linfeng Yang, Peilun Li, Shifei Chen, Haiyan Zheng

“…This paper presents a hybrid Graph Neural Network (GNN)–Linear Programming (LP) framework to accelerate the solution of large-scale Unit Commitment Problems (UCPs) while maintaining the quality of solutions. …”
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Enhancing Immoral Post Detection on Social Networks Using Knowledge Graph and Attention-Based BiLSTM Framework by Bibi Saqia, Khairullah Khan, Atta Ur Rahman, Sajid Ullah Khan, Mohammed Alkhowaiter, Wahab Khan, Ashraf Ullah

“…To bridge this gap, this work proposes the use of a Knowledge Graph (KG) for entity recognition and the extraction of semantic relationships in Social Network (SN) posts. …”
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ANALYSIS OF A DECADE OF STUDENT GRADES IN THE ELECTRICAL ENGINEERING DEPARTMENT AT SHARIF UNIVERSITY OF TECHNOLOGY USING GRAPH SIGNAL PROCESSING by Amirhossein Golshirazi, Reza Parhizkar, Arash Amini, Mohammad Mahdi Omati

“…Using the graph connections, we evaluate how well the grades of specific courses align with the overall performance of the students. …”
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FAIR Jupyter: A Knowledge Graph Approach to Semantic Sharing and Granular Exploration of a Computational Notebook Reproducibility Dataset by Samuel, Sheeba, Mietchen, Daniel

“…Here, we demonstrate how data that we had previously shared in bulk can be mobilized further through a knowledge graph that allows for much more granular exploration and interrogation. …”
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Transforming formal knowledge to language and graphs to promote mathematics learning: A repeated-measures mixed design quasi-experiment by Inka Sara Hähnlein, Clara Luleich, Philipp Reiter, Nils Waterstraat, Pablo Pirnay-Dummer

“…We use a new approach called natural-language conceptual Graph (NaGra), which translates mathematical formalism into natural language. …”
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Multi-atlas ensemble graph neural network model for major depressive disorder detection using functional MRI data by Nojod M. Alotaibi, Areej M. Alhothali, Manar S. Ali

“…Recent years have seen a rise in interest in graph neural networks (GNNs), which are deep neural architectures specifically designed to handle graph-structured data like rs-fMRI. …”
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Self-organizing dynamic research based on phase coherence graph autoencoders: Analysis of brain metastable states across the lifespan by Hao Guo, Yu-Xuan Liu, Yao Li, Qi-Li Guo, Zhi-Peng Hao, Yan-Li Yang, Jing Wei

“…We developed a framework based on Phase Coherence Graph Autoencoder (PCGAE) that employs graph autoencoders (GAE) for non-linear dimensionality reduction of phase coherence matrices. …”
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GRLGRN: graph representation-based learning to infer gene regulatory networks from single-cell RNA-seq data by Kai Wang, Yulong Li, Fei Liu, Xiaoli Luan, Xinglong Wang, Jingwen Zhou

“…Abstract Background A gene regulatory network (GRN) is a graph-level representation that describes the regulatory relationships between transcription factors and target genes in cells. …”
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