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

“…Multigraph contrastive learning, including both local and global graph contrastive learning, is used to help the embedding vectors better capture the latent topological features of the graph. …”
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MFHG-DDI: An Enhanced Hybrid Graph Method Leveraging Multiple Features for Predicting Drug–Drug Interactions by Xiang Li, Xiangmin Ji, Chengzhen Xu, Jie Hou, Xiaoyu Zhao, Guodong Peng

“…Predicting potential DDIs before administering medication to patients is crucial for drug development as it helps to prevent adverse drug reactions. Many effective DDI prediction methods have been proposed using graph neural networks; however, these methods only aggregate information from directly connected nodes restricted to a drug-related manner and fail to capture long-range dependencies in heterogeneous networks. …”
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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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EDG-Net: Edge-Enhanced Dynamic Graph Convolutional Network for Remote Sensing Scene Classification of Mining-Disturbed Land by Xianju Li, Pan Kong, Weitao Chen, Wenxi He, Jian Feng, Jiangyuan Wang

“…Finally, the convolutional and graph features are fused using a residual structure to obtain richer feature representations. …”
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An interpretive evaluation of entity summarization system by Qingxia LIU, Junyou LI, Gong CHENG

“…The task of entity summarization (ES) is to select an optimum subset from a large set of triples describing an entity in a knowledge graph.ES systems often integrate many and various ES features in a complex way.While state-of-the-art ES systems have been evaluated and compared by recent benchmarking efforts, it was unclear whether and how much each constituent ES feature had contributed to the performance of an ES system.An interpretive evaluation of ES systems was proposed.Two novel evaluation metrics were proposed, feature effectiveness ratio and feature significance ratio, to characterize how much ground-truth summaries and machine-generated summaries exhibit each ES feature.Their comparison would help to interpret the performance of an ES system.Based on three benchmarks, metrics with six popular ES features were implemented, and an interpretive evaluation of nine unsupervised ES systems and two supervised ES systems were presented.The code and data are open source.…”
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Solution Approach to the Minimum Spanning Tree Problem in Tsukamoto Fuzzy and Fermantean Fuzzy Environments by Desli de Haas, Citra Palembang, Aziz Rumakefing

“…Since the proposed Algorithm includes FFN ranking and Arithmetic Operations, we use the improved FFN scoring function to compare the edge weights of the graphs. With the help of Numerical examples, the solution technique for the proposed FFMST model is explained. …”
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SpaCcLink: exploring downstream signaling regulations with graph attention network for systematic inference of spatial cell–cell communication by Jingtao Liu, Litian Ma, Fen Ju, Chenguang Zhao, Liang Yu

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Federated SPARQL query performance evaluation for exploring disease model mouse: combining gene expression, orthology, and disease knowledge graphs by Tatsuya Kushida, Tarcisio Mendes de Farias, Ana C. Sima, Christophe Dessimoz, Hirokazu Chiba, Frederic B. Bastian, Hiroshi Masuya

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Understanding the Role of Humanistic Factors in Trade Network Evolution across the Belt and Road Initiative Countries Using the Exponential Random Graph Model by Jianjun Xu, Xiaowei Yang, Asif Razzaq

“…Firstly, we analyzed the structural characteristics of the import trade network across the 61 BRI countries and subsequently adopted the cross-sectional exponential random graph model (ERGM) and temporal ERGM to analyze the role of different humanistic factors in the evolution of import trade network from the static and dynamic perspectives, respectively. …”
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Enhancing leaf disease classification using GAT-GCN hybrid model by Shyam Sundhar, Riya Sharma, Priyansh Maheshwari, Suvidha Rupesh Kumar, T. Sunil Kumar

“…The research presented in this paper addresses this need by analyzing a hybrid model built using Graph Attention Network (GAT) and Graph Convolution Network (GCN) models. …”
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Exploring Graph Theory Mechanisms of Fluid Intelligence in the DLPFC: Insights From Resting‐State fNIRS Across Various Time Windows by Yuemeng Wang, Zhencai Chen, Ziqi Cai, Wenqun Ao, Qi Li, Ming Xu, Suyun Zhou

“…To clarify the temporal mechanisms of graph theory in measuring gF, this study investigated the relationship between graph theoretical indicators in the dorsolateral prefrontal cortex (DLPFC) and gF levels under various time windows. …”
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A multi-graph convolutional network method for Alzheimer’s disease diagnosis based on multi-frequency EEG data with dual-mode connectivity by Qingjie Xu, Qingjie Xu, Libing An, Haiqiang Yang, Haiqiang Yang, Keum-Shik Hong, Keum-Shik Hong

“…This study aims to address these limitations by developing a novel graph-based deep learning model that fully utilizes both functional and structural information from multi-frequency EEG data.MethodsThis paper introduces a Multi-Frequency EEG data-based Multi-Graph Convolutional Network (MF-MGCN) model for AD diagnosis. …”
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Evaluation of eco-driving performance of electric vehicles using driving behavior-enabled graph spectrums: A naturalistic driving study in China by Hui Zhang, Yiyue Luo, Naikan Ding, Toshiyuki Yamamoto, Chenming Fan, Chunhui Yang, Wei Xu, Chaozhong Wu

“…Then, the energy consumption graph was constructed using the energy loss of 100 ​km (EL) index. …”
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K-LM: Knowledge Augmenting in Language Models Within the Scholarly Domain by Vivek Kumar, Diego Reforgiato Recupero, Rim Helaoui, Daniele Riboni

“…Our experimental findings also help us conclude the importance of relevance of KG used over the quantity of injected RDF triples. …”
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A knowledge graph based remanufacturing equipment resource modeling method. [version 2; peer review: 1 approved, 2 approved with reservations] by Yuyao Guo, Lei Wang, Xuhui Xia, Binyuan Zhang, Xinlan Liu

“…Based on the top-down knowledge graph construction process, the RMER semantic association information of the ontology pattern and data layers was extracted and integrated to construct the RMER knowledge graph. …”
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Enhancing Urban Heatwave Response Planning via a Graph-Based Digital Twin Approach: Spatial Dependency Risk Analysis in Vienna City by George Stergiopoulos, Sozon A. Leventopoulos, Michalis Karamousadakis, Benjamin Schuster, Dimitris Gritzalis

“…Using Vienna’s city center as a case study, we integrate (i) high-resolution spatial data from Google on heat hazard, (ii) spatial population distribution from Vienna Open Data and (iii) META’s Vienna population datasets alongside (iv) critical road infrastructure from OpenStreetMaps, into a graph network model that captures cumulative risk paths that help determine the most critical city blocks needing intervention and the optimal allocation of resources for routing first responders to reach critical locations of sensitive populations. …”
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PRIVACY-PRESERVING REAL TIME TRACING SYSTEM FOR COVID-19 PATIENT USING GPS TECHNOLOGY by Nuril Lutvi Azizah, Uce Indahyanti

“…The method used in this study is a combination of graph theory and GPS tracing system on a gadget. …”
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Role of topological indices in predictive modeling and ranking of drugs treating eye disorders by Nazeran Idrees, Esha Noor, Saima Rashid, Fekadu Tesgera Agama

“…Abstract Topological indices (TIs) of chemical graphs of drugs hold the potential to compute important properties and biological activities leading to more thoughtful drug design. …”
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Ultrasound derived deep learning features for predicting axillary lymph node metastasis in breast cancer using graph convolutional networks in a multicenter study by Enock Adjei Agyekum, Wentao Kong, Doris Nti Agyekum, Eliasu Issaka, Xian Wang, Yong-zhen Ren, Gongxun Tan, Xuan Jiang, Xiangjun Shen, Xiaoqin Qian

“…Abstract The purpose of this study was to create and validate an ultrasound-based graph convolutional network (US-based GCN) model for the prediction of axillary lymph node metastasis (ALNM) in patients with breast cancer. …”
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Generative AI at the heart of the concerns of the renewable energy sector in Morocco: Development of AI assistants and analysis between vector database and rag using knowledge grap... by Bennani Anas, Ghafir Adil, Tali Abdelhak, El Hajjaji Soukaina

“…The Continuous progress in the field of generative artificial intelligence is now helping to change our behavior. It’s pushing us to think about how best to leverage this technology. …”
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