Condition monitoring of heterogeneous landslide deformation in spatio-temporal domain using advanced graph attention network by Huajin Li, Yu Zhu, Qiang Xu, Ran Tang, Chuanhao Pu, Yusen He

“…This research aims to develop an enhanced spatial-temporal monitoring system capable of capturing these complex deformation patterns. In this study, it presents a novel Graph Attention Network (GAT) framework that integrates multi-scale temporal embeddings, adaptive graph learning, and temporal self-attention mechanisms to simultaneously track localized stability variations and global deformation trends across monitoring points. …”
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Graph-Based Immune Checkpoint Inhibitor Response Prediction Model for Metastatic Renal Cell Carcinoma Patients by Sandra Alonso, Laura Hernandez-Lorenzo, Ignacio Duran, Cristina Rodriguez-Antona, Jesus Garcia-Donas, Jose L. Ayala

“…Then, we compressed these four different graph datasets with a graph embedding technique (Graph2Vec) and evaluated the embeddings as input for a supervised classification model (Random Forest) to predict the binarized progression-free survival (PFS). …”
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TSTA-GCN: trend spatio-temporal traffic flow prediction using adaptive graph convolution network by Xinlu Zong, Jiawei Guo, Fucai Liu, Fan Yu

“…A trend spatio-temporal adaptive graph convolution network (TSTA-GCN) model for metro passenger flow prediction is presented in this paper. …”
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Learning Content Model: from Concept Structuring to Adaptive Learning by J. V. Vainshtein, R. V. Esin, G. M. Tsibulsky

“…The presented model for constructing the learning content of the academic discipline differs from the wellknown ones by the presence of logical ordering of concepts based on the integration of logic methods of concept analysis, using logical and epistemological methods for correlating the volume and content of concepts with the methods of graph theory and hypergraphs. …”
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Leveraging prior knowledge to infer gene regulatory networks from single-cell RNA-sequencing data by Marco Stock, Corinna Losert, Matteo Zambon, Niclas Popp, Gabriele Lubatti, Eva Hörmanseder, Matthias Heinig, Antonio Scialdone

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Performance of an End-to-End Inventory Demand Forecasting Pipeline Using a Federated Data Ecosystem by Henrique Duarte Moura, Els de Vleeschauwer, Gerald Haesendonck, Ben De Meester, Lynn D’eer, Tom De Schepper, Siegfried Mercelis, Erik Mannens

“…Graph deep learning forecasting has the ability to comprehend intricate relationships, and it seamlessly tunes into the diverse, multi-retailer data present in a federated setup. …”
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MMAgentRec, a personalized multi-modal recommendation agent with large language model by Xiaochen Xiao

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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

“…In this paper, we present Latent Graph Attention (LGA), a computationally inexpensive (linear to the number of nodes) and stable modular framework for incorporating the global context in existing architectures. …”
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Accelerating multi-objective optimization of concrete thin shell structures using graph-constrained GANs and NSGA-II by Zhichun Fang, Xiuhong Wang, Yuyong Sun, M. A. Adibhashimi

“…Graph-constrained conditional Generative Adversarial Networks (GANs) and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) are used in the study. …”
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Efficient Vehicle Detection and Optimization in Multi-Graph Mode Considering Multi-Section Tracking Based on Geographic Similarity by Yue Chen, Jian Lu

“…Vehicle detection is an important part of modern intelligent transportation systems. At present, complex deep learning algorithms are often used for vehicle detection and tracking, but high-precision detection results are often obtained at the cost of time, and the existing research rarely considers optimization algorithms for vehicle information. …”
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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

“…To solve the problems related to manual segmentation such as time-cost, inaccuracy and subjectivity, automatic segmentation with deep learning methods is presented. This study aimed to develop an automatic brain tumor segmentation based on the combination of convolutional and graph neural networks to overcome the shortcomings of each network when they are used individually. …”
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Knowledge injection methods in question answering by D. V. Radyush

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Adaptive Global Dense Nested Reasoning Network into Small Target Detection in Large-Scale Hyperspectral Remote Sensing Image by Siyu Zhan, Yuxuan Yang, Muge Zhong, Guoming Lu, Xinyu Zhou

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Winter Wheat Yield Prediction Based on the ASTGNN Model Coupled with Multi-Source Data by Zhicheng Ye, Xu Zhai, Tianlong She, Xiaoyan Liu, Yuanyuan Hong, Lihui Wang, Lili Zhang, Qiang Wang

“…Therefore, we present an attention-based spatio-temporal Graph Neural Network (ASTGNN) model coupled with geospatial characteristics and multi-source data for improved accuracy of winter wheat yield estimation. …”
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DVAEGMM: Dual Variational Autoencoder With Gaussian Mixture Model for Anomaly Detection on Attributed Networks by Wasim Khan, Mohammad Haroon, Ahmad Neyaz Khan, Mohammad Kamrul Hasan, Asif Khan, Umi Asma Mokhtar, Shayla Islam

“…These approaches have network sparsity and data nonlinearity problems, and they do not even capture the intricate relationships between various information sources. Deep learning approaches like graph autoencoders are utilized to perform anomaly detection through obtaining node embeddings while dealing with the network nonlinearity and sparsity issues. …”
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Gait-to-Gait Emotional Human–Robot Interaction Utilizing Trajectories-Aware and Skeleton-Graph-Aware Spatial–Temporal Transformer by Chenghao Li, Kah Phooi Seng, Li-Minn Ang

“…In our system, the humanoid robot NAO can recognize emotions from human gaits through our Trajectories-Aware and Skeleton-Graph-Aware Spatial–Temporal Transformer (TS-ST) and respond with pre-set emotional gaits that reflect the same emotion as the human presented. …”
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Fault Diagnosis Method for Vacuum Contactor Based on Time-Frequency Graph Optimization Technique and ShuffleNetV2 by Haiying Li, Qinyang Wang, Jiancheng Song

“…The OTSU algorithm is then combined to crop the energy concentration area, and the size of these time-frequency graphs is optimized by 68.86%. Finally, considering the advantages of the channel split and channel shuffle methods, the ShuffleNetV2 network is adopted to improve the feature learning ability and identify fault categories. …”
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Identifying key genetic variants in Alzheimer’s disease progression using Graph Convolutional Networks (GCN) and biological impact analysis by Belal A. Hamed, Heba Mamdouh Farghaly, Ahmed Omar, Tarek Abd El-Hafeez

“…Abstract Alzheimer’s disease (AD) involves complex genetic interactions that remain challenging to model computationally. We present a novel deep learning framework integrating Single Nucleotide Polymorphism (SNP) data with Graph Convolutional Networks (GCNs) to predict gene-disease relationships in AD. …”
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Federated duelling deep Q‐network based collaborative energy scheduling for a power distribution network by Yanhong Yang, Wei Pei, Tianyi Xu, Dawei Wang, Abdelbari Redouane

“…Then, considering the application of Markov decision processes for energy scheduling, a spatial temporal graph convolutional network transformer based power generation packaging model for renewable energy sources was presented, and a collaborative energy scheduling strategy based on a federated duelling deep Q‐network was designed. …”
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A graph embedding‐based approach for automatic cyber‐physical power system risk assessment to prevent and mitigate threats at scale by Shining Sun, Hao Huang, Emily Payne, Shamina Hossain‐McKenzie, Nicholas Jacobs, H. Vincent Poor, Astrid Layton, Katherine Davis

“…By employing this graph embedding‐based approach, the authors present a structured and methodical framework for risk assessment in cyber‐physical systems. …”
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