CGTS: graph transformer-based anomaly detection in controller area networks by Xue Zhou, Guihe Qin, Yanhua Liang, Jiaru Song, Wanning Liu, Qingxin Liu

“…Abstract Anomaly detection in the Controller Area Network (CAN) bus is critical for ensuring the security and reliability of intelligent connected vehicles, which are increasingly prevalent. …”
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Synchronization-based graph spatio-temporal attention network for seizure prediction by Jie Xiang, Yanan Li, Xubin Wu, Yanqing Dong, Xin Wen, Yan Niu

“…Abstract Epilepsy is a common neurological disorder in which abnormal brain waves propagate rapidly in the brain in the form of a graph network during seizures, and seizures are extremely sudden. …”
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Pedestrian Trajectory Prediction Based on Dual Social Graph Attention Network by Xinhai Li, Yong Liang, Zhenhao Yang, Jie Li

“…While the existing methods frequently encounter difficulties in effectively quantifying the nuanced social relationships, we propose a novel dual social graph attention network (DSGAT) that systematically models multi-level interactions. …”
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Graph-based reinforcement learning for software-defined networking traffic engineering by Jingwen Lu, Chaowei Tang, Wenyu Ma, Wenjuan Xing

“…This paper proposes GRL-TE (Graph-based Reinforcement Learning for Traffic Engineering), a novel framework that achieves near-optimal performance while maintaining computational efficiency across diverse network scales. …”
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STFDSGCN: Spatio-Temporal Fusion Graph Neural Network Based on Dynamic Sparse Graph Convolution GRU for Traffic Flow Forecast by Jiahao Chang, Jiali Yin, Yanrong Hao, Chengxin Gao

“…To address this challenge, we propose a spatio-temporal fusion graph neural network based on dynamic sparse graph convolution GRU for traffic flow forecast (STFDSGCN), which incorporates a spatio-temporal attention fusion scheme with a gating mechanism. …”
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Cluster-based route convergence method based on network state graph model by PAN Chengsheng, LU Huangjie, SHI Huaifeng, WANG Yingzhi

“…To address the challenges of time-varying node connectivity and frequent link failures in tactical communication networks under conditions of strong adversarial and high-mobility operations, which result in frequent route convergence and short effective transmission times, a cluster-based route convergence method based on network state graph model (OSPF-CSG) was proposed. …”
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Design of wavelength rotation graph model applied in wavelength switching optical network by ZHAO Ji-jun1, ZHANG Shu-guang1, ZHAO Wen-yu2

“…Based on distributing uniformly virtual network topology links and associated wavelengths to the surface of rotating sphere,a new wavelength rotation graph model of wavelength switching optical network（WSON） was constructed,and a creative method to solve the routing and wavelength assignment（RWA） problems was proposed.The simulation results show that if the wavelength number of each link is set separately to 4 and 8,the average blocking probability of the RWA based on the rotation graph model reduces by 5.03% and 9.71%,and the average resource utilization increases by 3.3% and 1.54% respectively,as compared with the existing model.This method would be useful for solving the problem of optical network RWA which has wavelength conversion capabilities.…”
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A Visibility Graph Approach to CNY Exchange Rate Networks and Characteristic Analysis by Can-Zhong Yao, Ji-Nan Lin

“…First, we transform central parity rate time series of US dollar, Euro, Yen, and Sterling against CNY into exchange rate networks with visibility graph algorithm and find consistent topological characteristics in four exchange rate networks, with their average path lengths 5 and average clustering coefficients 0.7. …”
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Few-Shot Named Entity Recognition Based on the Collaborative Graph Attention Network by Haoran Niu, Zhaoman Zhong

“…To overcome this, we introduce a novel few-shot NER encoder based on a Collaborative Graph Attention network (ColGAT). This encoder utilizes a collaborative graph-based data augmentation mechanism to thoroughly extract latent semantic features of entities within sentences, enabling precise entity recognition. …”
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Pedestrian trajectory prediction model based on self-supervised spatiotemporal graph network by Shiji Yang, Xuezhong Xiao

“…Thus, a pedestrian trajectory prediction model based on a self - supervised spatiotemporal graph network is proposed. Firstly, in the process of spatiotemporal graph modeling, this model introduces hop interaction instead of node interaction to update node features, which greatly reduces the times of graph convolution operations, alleviates the problem of feature smoothing, and greatly improves the accuracy of prediction. …”
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Pedestrian Trajectory Prediction Based on Transformer and Multi-relation Graph Convolutional Networks by LIU Guihong, ZHOU Zongrun, MENG Xiangfu

“…To address this, a pedestrian trajectory prediction model combining Transformer and multi-relation graph convolutional network (GCN) is proposed. The model is composed of interaction capture module, anchor control module, and trajectory refinement module. …”
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Trajectory- and Friendship-Aware Graph Neural Network with Transformer for Next POI Recommendation by Chenglin Yu, Lihong Shi, Yangyang Zhao

“…Our approach begins with the construction of trajectory flow graphs using graph convolutional networks (GCNs) to globally capture POI correlations across both spatial and temporal dimensions. …”
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Efficient Integration of Reinforcement Learning in Graph Neural Networks-Based Recommender Systems by Abdurakhmon Sharifbaev, Mikhail Mozikov, Hakimjon Zaynidinov, Ilya Makarov

“…Although RL has been applied in recommendation systems, the integration of graph neural networks (GNNs) within this framework remains underexplored. …”
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Pedestrian Trajectory Prediction via Window Attention and Spatial Graph Interaction Network by Xiang Gu, Chao Li, Jie Yang, Jing Wang, Qiwei Huang

“…In the spatial dimension, a hierarchical heterogeneous GCN (graph convolutional network) is constructed, combining pedestrian dynamic interaction graphs and scene semantic static graphs. …”
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Spatio-Temporal Graph Neural Networks for Streamflow Prediction in the Upper Colorado Basin by Akhila Akkala, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi, Pouya Hosseinzadeh, Ayman Nassar

“…This study presents a spatio-temporal graph neural network (STGNN) model for streamflow prediction in the Upper Colorado River Basin (UCRB), integrating graph convolutional networks (GCNs) to model spatial connectivity and long short-term memory (LSTM) networks to capture temporal dynamics. …”
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Adopting Graph Neural Networks to Understand and Reason About Dynamic Driving Scenarios by Peng Su, Conglei Xiang, Dejiu Chen

“…This paper presents a framework that adopts Graph Neural Networks (GNN) to describe and reason about dynamic driving scenarios via analyzing graph-based data based on collected sensor inputs. …”
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Rolling Bearing Fault Diagnosis via Temporal-Graph Convolutional Fusion by Fan Li, Yunfeng Li, Dongfeng Wang

“…Simultaneously, frequency-domain features obtained via Fast Fourier Transform (FFT) were used to construct a K-Nearest Neighbors (KNN) graph, which was processed by a Graph Convolutional Network (GCN) to identify spatial correlations. …”
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PLGNN: graph neural networks via adaptive feature perturbation and high-way links by Meixia He, Peican Zhu, Yang Liu, Keke Tang

“…Abstract Graph neural networks (GNNs) have exhibited remarkable performance in addressing diverse graph learning tasks. …”
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An Efficient Topology Construction Scheme Designed for Graph Neural Networks in Hyperspectral Image Classification by Yu Zhang, Xin Li, Yaoqun Xu

“…Superpixel-based Graph Neural Networks (GNNs) have achieved remarkable success in hyperspectral image (HSI) classification tasks, primarily due to their ability to capture the implicit topological structure in the data while maintaining low computational complexity by propagating information between spatially adjacent superpixels. …”
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Multi-scale spatio-temporal graph neural network for urban traffic flow prediction by Hui Chen, Jian Huang, Yong Lu, Jijie Huang

“…In response to the above challenges, this paper proposes a novel Spatio-Temporal Graph neural network with Multi-timeScale (abbreviated as STGMS). …”
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