Time Labeled Visibility Graph for Privacy-Preserved Physiological Time Series Classification

Xiu, Yuxuan; Ren, Xinyue; Zhang, Ting; Chen, Yanyu; Jiang, Li; Li, Duo; Wang, Xingjun; Zhao, Liang; Chan, Wai Kin Victor

doi:10.1109/icccbda55098.2022.9778887

Cited by 7 publications

(2 citation statements)

References 17 publications

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“…As presented by [26] by transforming TS based on horizontal VG representation they developed a method for identifying anomalous patterns in univariate TS data in smart grid systems. Two other works utilize VG representations of univariate TS, first for ECG signals [27], while [28] integrated a VG and trained it together with GNN classification model for radio signal modulation. These works highlight the potential of applying VG approach for pattern recognition in TS, and to the best of our knowledge, we are the first to utilise it for anomaly detection in wireless signals.…”

Section: Graph Neural Network For Time Series Datamentioning

confidence: 99%

Visibility Graph-Based Wireless Anomaly Detection for Digital Twin Edge Networks

Bertalanič,

Hribar,

Fortuna

2024

IEEE Open J. Commun. Soc.

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Network softwarization, which shifts hardware-centric functions to software implementations, is essential for enhancing the agility of cellular and non-cellular wireless networks. This change, while raising reliability concerns, also improves system monitoring through digital twins. One example is the Digital Twin Edge Networks (DITEN), which enhances real-time analysis and quick anomaly response in the unpredictable last-mile wireless edge network environment. Positioned close to the physical infrastructure, DITEN is effective in rapidly detecting and addressing network irregularities. This study presents an advanced anomaly detection method for DITEN, employing time-series data conversion to Visibility Graph (VG) and utilising Graph Neural Network (GNN), with a focus on addressing disruptions at the network's physical layer. Our proposed method outperforms the State-of-the-art (SOTA) time series Deep Learning (DL) classification architecture by 13 percentage points and achieves ≈110 times higher computational efficiency. Furthermore, our method surpasses the classical Machine Learning (ML) model Hive-Cote2 by 2.2 percentage points while maintaining ≈5.9 times better computational efficiency. The model also outperforms the current best SOTA imaging model by up to 6 percentage points and the leading graph-based method by up to 10 percentage points, both with significantly lower Computational Complexity (CC) of ≈210-times and ≈4-times, respectively. Additionally, we show that when 1000 concurrent requests arrive, the proposed method achieves a mean response latency of less than or equal to 60 seconds across three setups. Finally, we demonstrate that the combination of Natural Visibility Graph (NVG) and the proposed GNN model provides interpretable insights by observing gradient changes.

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Section: Graph Neural Network For Time Series Datamentioning

confidence: 99%

Visibility Graph-Based Wireless Anomaly Detection for Digital Twin Edge Networks

Bertalanič,

Hribar,

Fortuna

2024

IEEE Open J. Commun. Soc.

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“…The complex network provides a powerful theoretical tool for the abstract characterization of many real-world systems composed of various objects and mutual relationships, such as technological [13,64,65], biological [66][67][68][69], and social systems [70][71][72][73][74]. A wide range of real-world networks have the fractal property, which could be roughly described as, "The network looks similar under different magnification levels" [75].…”

Section: Fractal Analysis Of Complex Networkmentioning

confidence: 99%

Self-Similar Growth and Synergistic Link Prediction in Technology-Convergence Networks: The Case of Intelligent Transportation Systems

et al. 2023

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Self-similar growth and fractality are important properties found in many real-world networks, which could guide the modeling of network evolution and the anticipation of new links. However, in technology-convergence networks, such characteristics have not yet received much attention. This study provides empirical evidence for self-similar growth and fractality of the technology-convergence network in the field of intelligent transportation systems. This study further investigates the implications of such fractal properties for link prediction via partial information decomposition. It is discovered that two different scales of the network (i.e., the micro-scale structure measured by local similarity indices and the scaled-down structure measured by community-based indices) have significant synergistic effects on link prediction. Finally, we design a synergistic link prediction (SLP) approach which enhances local similarity indices by considering the probability of link existence conditional on the joint distribution of two scales. Experimental results show that SLP outperforms the benchmark local similarity indices in most cases, which could further validate the existence and usefulness of the synergistic effect between two scales on link prediction.

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Boosting short term electric load forecasting of high & medium voltage substations with visibility graphs and graph neural networks

Giamarelos,

Zois

2024

Sustainable Energy, Grids and Networks

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Time Labeled Visibility Graph for Privacy-Preserved Physiological Time Series Classification

Cited by 7 publications

References 17 publications

Visibility Graph-Based Wireless Anomaly Detection for Digital Twin Edge Networks

Visibility Graph-Based Wireless Anomaly Detection for Digital Twin Edge Networks

Self-Similar Growth and Synergistic Link Prediction in Technology-Convergence Networks: The Case of Intelligent Transportation Systems

Boosting short term electric load forecasting of high & medium voltage substations with visibility graphs and graph neural networks

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