Hybrid AI for Predictive Cyber Risk Assessment: Federated Graph-Transformer Architecture With Explainability by Jaime Govea, Rommel Gutierrez, William Villegas-Ch, Alexandra Maldonado Navarro

“…This paper proposes a hybrid model for predictive cyber risk assessment that integrates Graph Neural Networks (GNNs) for relational pattern modeling, a Transformer-based language model (CyberBERT) for semantic representation of logs and traffic data, and a Federated Learning framework to preserve data privacy during training. …”
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Research on multi dimensional feature extraction and recognition of industrial and mining solid waste images based on mask R-CNN and graph convolutional networks by Shuqin Wang, Na Cheng, Yan Hu

“…Abstract Aiming at the problems of traditional methods for multi-dimensional feature extraction of industrial and mining solid waste images, such as single feature extraction, difficult fusion, missing high-order features, weak generalization ability and low computational efficiency, an innovative solution combining Mask R-CNN with Graph Convolutional Networks (GCN) was proposed to achieve automatic, multi-dimensional and efficient feature extraction. …”
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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

“…Results We present GNODEVAE, a novel architecture integrating Graph Attention Networks (GAT), Neural Ordinary Differential Equations (NODE), and Variational Autoencoders (VAE) for comprehensive single-cell analysis. …”
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A Hybrid Long Short-Term Memory-Graph Convolutional Network Model for Enhanced Stock Return Prediction: Integrating Temporal and Spatial Dependencies by Songze Shi, Fan Li, Wei Li

“…This study proposes a hybrid model integrating long short-term memory (LSTM) networks and graph convolutional networks (GCNs) to enhance accuracy by capturing both temporal dynamics and spatial inter-stock relationships. …”
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Antarctic Sea Ice Extraction for Remote Sensing Images via Modified U-Net Based on Feature Enhancement Driven by Graph Convolution Network by Wu Feng, Xiulin Geng, Xiaoyu He, Miao Hu, Jie Luo, Meihua Bi

“…Graph convolutional networks are utilized to aggregate the features at each pixel, enabling the rapid capture of global context, enhancing the semantic richness of the features, and improving the accuracy of sea ice extraction through graph reconstruction. …”
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Design of an improved graph-based model integrating LSTM, LoRaWAN, and blockchain for smart agriculture by Ravi Kumar Munaganuri, Narasimha Rao Yamarthi, Sai Chandana Bolem

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PM2.5 prediction and its influencing factors in the Beijing-Tianjin-Hebei urban agglomeration using spatial temporal graph convolutional networks by Yawen Zhao

“…To address this, this study uses spatiotemporal analysis and Spatial Temporal Graph Convolutional Networks (ST-GCN) to evaluate the variation and driving factors of PM _2.5 concentrations in the Beijing-Tianjin-Hebei (BTH) urban agglomeration from 2014 to 2024, and to make predictions. …”
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Impact Mechanisms of Canals on Hydrological Connectivity of River and Lake System Interconnection Networks by SUN Guangru, QIU Guoqiang, JIANG Xiaojun, WANG Xuewei, WANG Jing

“…［Methods］ The area around Laizhou Bay was selected as the study area. The SWAT+model and graph theory method were employed to analyze the hydrological connectivity of the river and lake system interconnection network under two scenarios, with and without the canal. …”
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Graph-based analysis of histopathological images for lung cancer classification using GLCM features and enhanced graph by Imam Dad, JianFeng He, Zulqarnain Baloch

“…This study advances computational pathology by unifying Graph Neural Networks (GNN) with interpretable feature engineering, offering a scalable, efficient solution for cancer subtype classification. …”
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Lightweight pose estimation spatial-temporal enhanced graph convolutional model for miner behavior recognition by WANG Jianfang, DUAN Siyuan, PAN Hongguang, JING Ningbo

“…Skeleton-sequence-based behavior recognition models are characterized by fast processing speeds, low computational requirements, and simple structures. Graph convolutional networks (GCNs) have advantages in processing skeleton sequence data. …”
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Multi-component attention graph convolutional neural network for QoS-aware cloud job scheduling and resource management enhancing efficiency and performance in cloud computing by Deepak Dharrao, Sarika Deokate, Nagamma Hudgi, Shrinivas Shirkande, Vinod Mahajan, Madhuri Pangavhane, Anupkumar Bongale

“…Initially, QoS based Job scheduling and resource management using Multi-Component Attention Graph Convolutional Neural Network (MCAGCN) to maximize Success Rates in the Cloud. …”
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GAPO: A Graph Attention-Based Reinforcement Learning Algorithm for Congestion-Aware Task Offloading in Multi-Hop Vehicular Edge Computing by Hongwei Zhao, Xuyan Li, Chengrui Li, Lu Yao

“…The algorithm models the dynamic VEC network as an attributed graph and utilizes a graph neural network (GNN) to learn a network state representation that captures the global topological structure and node contextual information. …”
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GCNTrack: A Pig-Tracking Method Based on Skeleton Feature Similarity by Zhaoyang Yin, Zehua Wang, Junhua Ye, Suyin Zhou, Aijun Xu

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A complex network perspective on spatiotemporal evolution of extreme precipitation over the middle and lower reaches of the Yangtze river by Ziyi Hu, Aixia Feng, Changgui Gu, Peng Zhao, Qiguang Wang

“…In this study, we aimed to analyze the spatiotemporal evolution characteristics of extreme precipitation based on visible graph network and state transition networks at different percentile thresholds, and to analyze and compare with the topology, network type, anomalous year, trend of the change stage, key modes, and the future evolution trend. …”
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Threat Detection Framework Based on Industrial Internet of Things Logs by Shuqin Zhang, Yong Wang, Xinyu Su

“…It then employs an anomaly detection method based on a temporal graph neural network, which considers events’ structural and temporal characteristics for a thorough analysis of logs. …”
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DGCLCMI: a deep graph collaboration learning method to predict circRNA-miRNA interactions by Chao Cao, Mengli Li, Chunyu Wang, Lei Xu, Quan Zou, Yansu Wang, Wu Han

“…Next, we present a joint model that combines an improved neural graph collaborative filtering method with a feature extraction network for optimization. …”
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AttenFlow: Context-Aware Architecture with Consensus-Based Retrieval and Graph Attention for Automated Document Processing by Xianfeng Zhang, Bin Hu, Shukan Liu, Qiao Sun, Lin Chen

“…Second, we develop adversarial mutual-attention hybrid-dimensional graph attention network (AM-HDGAT) for text, which transforms document classification by modeling inter-document relationships through graph structures while integrating high-dimensional semantic features and low-dimensional statistical features through mutual-attention mechanisms. …”
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Analyzing and forecasting the dynamics of Internet resource user sentiments based on the Fokker–Planck equation by J. P. Perova, S. A. Lesko, A. A. Ivanov

“…The current state of the comment network graph can be described using a vector whose elements are the average value of the mediation coefficient, the average value of the clustering coefficient, and the proportion of users in a corresponding state. …”
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EEG-Based Micro-Expression Recognition: Flexible Brain Network Reconfiguration Supporting Micro-Expressions Under Positive Emotion by Chen J, Zhao X, Xiong Z, Liu G

“…However, the neural mechanisms of micro-expressions remain unclear, limiting the development of EEG-based recognition technology.Methods: We explored the brain reorganization mechanisms of micro-expressions (compared with macro-expressions and neutral expressions) under positive emotions across global networks, functional network modules, and hub brain regions using EEG, graph theory analysis, and functional connectivity.Results: In global network, micro-expressions demonstrated higher network efficiency, clustering coefficient, and local efficiency, along with shorter average path lengths. …”
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Hybrid Graph Representation and Learning Framework for High-Level Synthesis Design Space Exploration by Pouya Taghipour, Eric Granger, Yves Blaquiere

“…Learning-based methods, particularly graph neural networks (GNNs), have shown considerable potential in addressing HLS QoR/DSE problems by modeling the mapping function from control data flow graphs (CDFGs) of HLS designs to their logic, enabling early estimation of QoR during the compilation phase of the hardware design flow. …”
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