Block Encryption and Decryption of a Sentence Using Decomposition of the Turan Graph by C. Beaula, P. Venugopal, B. Praba

“…Recently, graph theory concepts are employed in cryptography to make it stronger. The usage of complex graphs in cryptosystems makes it difficult to hack. …”
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BHGNN-RT: Capturing bidirectionality and network heterogeneity in graphs. by Xiyang Sun, Fumiyasu Komaki

“…Graph neural networks (GNNs) have shown great promise for representation learning on complex graph-structured data, but existing models often fall short when applied to directed heterogeneous graphs. …”
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Generalization of Ramsey Number for Cycle with Pendant Edges by Jagjeet Jakhar, Monu Moun, Youngsoo Seol, Majeed Ahmad Yousif, Muhammad Amer Latif, Pshtiwan Othman Mohammed

“…The results presented contribute to the broader understanding of Ramsey theory and serve as a foundation for future research on generalized Ramsey numbers in complex graph structures.…”
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Navigating Intelligence: A Survey of Google OR‐Tools and Machine Learning for Global Path Planning in Autonomous Vehicles by Alexandre Benoit, Pedram Asef

“…GPP is essential for ROMIE's optimal performance, which is translated into solving the traveling salesman problem, a complex graph theory challenge that is crucial for determining the most effective route to cover all sampling locations in a mining field. …”
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METAGRAPH THEORY AS A BASIS FOR MODELING RELEVANT MEDIA DISCOURSE by Yuriy E. Gapanyuk

“…This article is devoted to modeling media discourse based on a combination of a complex graph model and a multidimensional model. Despite significant advances in the field of neural network text processing, the task of modeling text and media discourse remains relevant. …”
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Cross-attention graph neural networks for inferring gene regulatory networks with skewed degree distribution by Jiaqi Xiong, Nan Yin, Shiyang Liang, Haoyang Li, Yingxu Wang, Duo Ai, Jingjie Wang

“…It employs a cross-attention mechanism and a dual complex graph embedding approach to manage the skewed degree distribution, ensuring precise prediction of regulatory relationships and their directionality. …”
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Integration of molecular coarse-grained model into geometric representation learning framework for protein-protein complex property prediction by Yang Yue, Shu Li, Yihua Cheng, Lie Wang, Tingjun Hou, Zexuan Zhu, Shan He

“…While most existing algorithms represent PPI complex graph structures at the atom-scale or residue-scale, these representations can be computationally expensive or may not sufficiently integrate finer chemical-plausible interaction details for improving predictions. …”
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Variational graph autoencoder for reconstructed transcriptomic data associated with NLRP3 mediated pyroptosis in periodontitis by Pradeep K. Yadalam, Prabhu Manickam Natarajan, Carlos M. Ardila

“…VGAE, a deep learning model, captures complex graph relationships for tasks like link prediction and edge detection. …”
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Federated Subgraph Learning via Global-Knowledge-Guided Node Generation by Yuxuan Liu, Zhiming He, Shuang Wang, Yangyang Wang, Peichao Wang, Zhangshen Huang, Qi Sun

“…Federated graph learning (FGL) is a combination of graph representation learning and federated learning that utilizes graph neural networks (GNNs) to process complex graph-structured data while addressing data silo issues. …”
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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 framework processes mel multi-band spectro-temporal representations through a ViG architecture that models complex graph-based relationships between spectral and temporal components, trained using a supervised contrastive objective that learns discriminative representations distinguishing PD-affected from healthy speech patterns. …”
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Application of improved graph convolutional network for cortical surface parcellation by Jia Tan, Xiaomei Ren, Yong Chen, Xianju Yuan, Feiba Chang, Rui Yang, Chengqun Ma, Xiaoyu Chen, Miao Tian, Wei Chen, Zihong Wang

“…ADGCN consists of a deep graph convolutional layer with a symmetrical U-shaped structure, which enables it to effectively transmit detailed information of the original brain map and learn the complex graph structure, help the network enhance feature extraction capability. …”
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Attention-Enhanced Hybrid Automatic Modulation Classification for Advanced Wireless Communication Systems: A Deep Learning-Transformer Framework by Sam Ansari, Khawla A. Alnajjar, Sohaib Majzoub, Eqab Almajali, Anwar Jarndal, Talal Bonny, Abir Hussain, Soliman Mahmoud

“…In contrast to existing hybrid or purely sequential architectures, this design attains high classification fidelity without the need for complex graph-based structures or stacked attention mechanisms, thereby enhancing both model interpretability and practical deployment feasibility. …”
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Fractals via Generalized Jungck–S Iterative Scheme by Zhihua Chen, Muhammad Tanveer, Waqas Nazeer, Jing Wu

“…Moreover, we present some complex graphs of Julia and Mandelbrot sets using the derived orbit and discuss their nature in detail.…”
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On the Stability of the Kubernetes Horizontal Autoscaler Control Loop by Berta Serracanta, Andor Lukacs, Alberto Rodriguez-Natal, Albert Cabellos, Gabor Retvari

“…Additionally, we extend our model to the whole service graph to understand how individual scaling decisions influence the complex graphs of cloud applications.…”
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Node-Centric Pruning: A Novel Graph Reduction Approach by Hossein Shokouhinejad, Roozbeh Razavi-Far, Griffin Higgins, Ali A. Ghorbani

“…This paper introduces an innovative graph reduction technique, Node-Centric Pruning (NCP), designed to simplify complex graphs while preserving their essential structural properties, thereby enhancing the scalability and maintaining performance of downstream Graph Neural Networks (GNNs). …”
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A deep learning framework for predicting disease-gene associations with functional modules and graph augmentation by Xianghu Jia, Weiwen Luo, Jiaqi Li, Jieqi Xing, Hongjie Sun, Shunyao Wu, Xiaoquan Su

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