Suchergebnisse - temporal graph autoencoder

TGAE: Temporal Graph Autoencoder for Travel Forecasting von Wang, Qiang, Jiang, Hao, Qiu, Meikang, Liu, Yifeng, Ye, Dongsheng

“… To confront these challenges, we treat the dynamic traffic networks as multiple weighted directed network snapshots and propose a graph-based deep learning framework, Temporal Graph Autoencoder (TGAE …”
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A Spatial-Temporal Variational Graph Attention Autoencoder Using Interactive Information for Fault Detection in Complex Industrial Processes von Lv, Mingjie, Li, Yonggang, Liang, Huiping, Sun, Bei, Yang, Chunhua, Gui, Weihua

“… A spatial-temporal variational graph attention autoencoder (STVGATE) using interactive information is proposed for fault detection, which aims to effectively capture the spatial and temporal features of the interconnected …”
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Multivariate time series anomaly detection with variational autoencoder and spatial–temporal graph network von Guan, Siwei, He, Zhiwei, Ma, Shenhui, Gao, Mingyu

“… –temporal graph networks and variational autoencoder (VAE). It employs …”
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A Spatio‐Temporal Enhanced Graph‐Transformer AutoEncoder embedded pose for anomaly detection von Zhu, Honglei, Wei, Pengjuan, Xu, Zhigang

“… ‐temporal dependencies of non‐Euclidean data such as human skeleton graphs, and the autoencoder based on this basic unit is widely used to model sequence features …”
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Human-related anomalous event detection via spatial-temporal graph convolutional autoencoder with embedded long short-term memory network von Li, Nanjun, Chang, Faliang, Liu, Chunsheng

“… Our network is established on a Spatial-temporal Graph Convolutional …”
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Spatio-temporal graph convolutional autoencoder for transonic wing pressure distribution forecasting von Immordino, Gabriele, Vaiuso, Andrea, Da Ronch, Andrea, Righi, Marcello

“… This study presents a framework for predicting unsteady transonic wing pressure distributions due to pitch and plunge movement, integrating an autoencoder architecture with graph convolutional …”
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Graph autoencoder with mirror temporal convolutional networks for traffic anomaly detection von Ren, Zhiyu, Li, Xiaojie, Peng, Jing, Chen, Ken, Tan, Qushan, Wu, Xi, Shi, Canghong

“… In this paper, we propose a mirror temporal graph autoencoder (MTGAE) framework to explore anomalies and capture unseen nodes and the spatiotemporal correlation between nodes in the traffic network …”
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Convolutional Graph Autoencoder: A Generative Deep Neural Network for Probabilistic Spatio-Temporal Solar Irradiance Forecasting von Khodayar, Mahdi, Mohammadi, Saeed, Khodayar, Mohammad E., Wang, Jianhui, Liu, Guangyi

“… This probabilistic data generation model, i.e., convolutional graph autoencoder (CGAE), is devised based on the localized first-order approximation …”
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Graph Masked Autoencoder for Spatio-Temporal Graph Learning von Zhang, Qianru, Wang, Haixin, Siu-Ming Yiu, Yin, Hongzhi

“… To address these challenges, we propose a novel spatio-temporal graph masked autoencoder paradigm that explores generative self-supervised learning for effective spatio-temporal data augmentation …”
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Edge-Focused Temporal Graph Autoencoders for Anomalous Link Prediction in OT Networks von Howe, Alex, Papa, Mauricio

“… The proposed approach proposes a novel edge-focused temporal graph autoencoder that explicitly models edge features alongside temporal variations to improve intrusion detection performance …”
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Spatial-Temporal Graph Discriminant AutoEncoder for Traffic Congestion Forecasting von Peng, Jiaheng, Guan, Tong, Liang, Jun

“… In this paper, we propose a novel algorithm, the Spatial-Temporal Graph Discriminant Autoencoder (STGDAE …”
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Temporal Graph Convolutional Autoencoder based Fault Detection for Renewable Energy Applications von Arifeen, Murshedul, Petrovski, Andrei

“… To address this issue, we propose an autoencoder model that uses a temporal graph convolutional layer to detect faults in the energy generation process …”
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An Integrated Temporal Graph Neural Network and Autoencoder Model for Real-Time Credit Card Fraud Detection von Aich, Mrinmoy, Oggu, Vijaya Bhaskar, Sankaran, Mohan, H K, Keerthi, Sataar, Zahrah

“… However, existing Graph Neural Network (GNN) to detect relational fraud patterns across entities and autoencoder to identify anomalous transactions struggles …”
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A Comprehensive Survey on Graph Neural Networks von Wu, Zonghan, Pan, Shirui, Chen, Fengwen, Long, Guodong, Zhang, Chengqi, Yu, Philip S.

“… -Euclidean domains and are represented as graphs with complex relationships and interdependency between objects …”
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Spatio-Temporal Denoising Graph Autoencoders with Data Augmentation for Photovoltaic Timeseries Data Imputation von Fan, Yangxin, Yu, Xuanji, Wieser, Raymond, Meakin, David, Shaton, Avishai, Jean-Nicolas Jaubert, Flottemesch, Robert, Howell, Michael, Braid, Jennifer, Bruckman, Laura S, French, Roger, Wu, Yinghui

“… This paper proposes a novel Spatio-Temporal Denoising Graph Autoencoder (STD-GAE) framework to impute missing PV Power Data …”
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A Novel Unsupervised Structural Damage Detection Method Based on TCN-GAT Autoencoder von Ni, Yanchun, Jin, Qiyuan, Hu, Rui

“… This paper proposes an autoencoder model integrating Temporal Convolutional Networks (TCN) and Graph Attention Networks (GAT …”
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Efficient Learning-Based Graph Simulation for Temporal Graphs von Xiang, Sheng, Xu, Chenhao, Cheng, Dawei, Wang, Xiaoyang, Zhang, Ying

“… In real-life applications, e.g. social science, biology, and chemistry, many graphs are composed of a series of evolving graphs (i.e., temporal graphs …”
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A novel dynamic spatio-temporal graph based condition monitoring framework for consistency retention of digital twin von Wang, Xiaofeng, Yan, Jihong, Xu, Xun

“… On this basis, unsupervised learning is further combined to form a dynamic spatio-temporal graph based condition monitoring framework for DT consistency retention …”
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Reinforcement Learning and Spatio-Temporal Graph Neural Networks for Alzheimer's Disease Progression Prediction with Variational Autoencoder-Based Data Imputation von Shukla, Garima, Awasthi, Vanshaj, Roy, Sampurna, Iyer, Rajiv, Balusamy, Balamurugan, Gulhane, Monali

“… In this work, we introduce a reinforcement learning-augmented spatio-temporal graph neural network (RL-STGNN …”
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Network Anomaly Detection Integrating Dynamic Graph Embedding and Transformer Autoencoder von ZHANG Anqin, DING Zhifeng

“… Most existing graph-embedding-based methods are designed for static graphs and neglect fine-grained temporal information, thus failing to capture the continuity of dynamic network behaviors …”
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