Weighted Visibility Graph With Complex Network Features in the Detection of Epilepsy

Supriya, Supriya; Siuly, Siuly; Wang, Hua; Cao, Tru H.; Zhang, Yanchun

doi:10.1109/access.2016.2612242

Cited by 166 publications

(85 citation statements)

References 32 publications

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“…In this case, the horizontal visibility satisfies an ordering criterion. The visibility algorithm is simple to implement and has been applied in many different fields (e.g., [39,40,41,42,43]), including fluid flows [31,29,30,32,44,45]. However, the visibility approach has some drawbacks, related to the fact that it is invariant under affine transformations [15] (i.e., rescaling and translation of both horizontal and vertical axes), so this could lead to a lost of information in mapping the time-series.…”

Section: Mapping Time-series Into Network: the Visibility Algorithmmentioning

confidence: 99%

Visibility graph analysis of wall turbulence time-series

2018

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The spatio-temporal features of the velocity field of a fully-developed turbulent channel flow are investigated through the natural visibility graph (NVG) method, which is able to fully map the intrinsic structure of the time-series into complex networks. Time-series of the three velocity components, (u,v,w), are analyzed at fixed grid-points of the whole three-dimensional domain. Each time-series was mapped into a network by means of the NVG algorithm, so that each network corresponds to a grid-point of the simulation. The degree centrality, the transitivity and the here proposed mean link-length were evaluated as indicators of the global visibility, inter-visibility, and mean temporal distance among nodes, respectively. The metrics were averaged along the directions of homogeneity (x, z) of the flow, so they only depend on the wall-normal coordinate, y^+. The visibility-based networks, inheriting the flow field features, unveil key temporal properties of the turbulent time-series and their changes moving along y^+. Although intrinsically simple to be implemented, the visibility graph-based approach offers a promising and effective support to the classical methods for accurate time-series analyses of inhomogeneous turbulent flows

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Section: Mapping Time-series Into Network: the Visibility Algorithmmentioning

confidence: 99%

Visibility graph analysis of wall turbulence time-series

2018

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“…Wang et al [39] proposed a heterogeneous review graph to capture the relationships among reviewers, reviewers and stores, and then put forward an iterative model to identify suspicious reviewers. Supriya et al [6] made the epileptic EEG signals transform into the complex network and then used the statistical properties to detect the epilepsy.…”

Section: Related Workmentioning

confidence: 99%

“…Since complex network provides a powerful mechanism for capturing the interactive relationships among study objects, it has been an effective method for relational expression of structured data [5]- [7], especially the time series data. For instance, Supriya et al [6] translated the epileptic EEG signal time series into complex network, and then used the statistical properties of complex network to detect the epilepsy. Whereas in Internet, based on complex networks theory, the complexity of Internet topologies have been widely studied [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Using Intuitionistic Fuzzy Set for Anomaly Detection of Network Traffic From Flow Interaction

Wang

Zhang

et al. 2018

IEEE Access

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We present a method to detect anomalies in a time series of flow interaction patterns. There are many existing methods for anomaly detection in network traffic, such as number of packets. However, there is non established method detecting anomalies in a time series of flow interaction patterns that can be represented as complex network. Firstly, based on proposed multivariate flow similarity method on temporal locality, a complex network model (MFS-TL) is constructed to describe the interactive behaviors of traffic flows. Having analyzed the relationships between MFS-TL characteristics, temporal locality window and multivariate flow similarity critical threshold, an approach for parameter determination is established. Having observed the evolution of MFS-TL characteristics, three non-deterministic correlations are defined for network states (i.e. normal or abnormal). Furthermore, intuitionistic fuzzy set (IFS) is introduced to quantify three non-deterministic correlations, and then a anomaly detection method is put forward for single characteristic sequence. To build an objective IFS, we design a Gaussian distribution-based membership function with a variable hesitation degree. To determine the mapping of IFS's clustering intervals to network states, a distinction index is developed. Then, an IFS ensemble method (IFSE-AD) is proposed to eliminate the impacts of the inconsistent about MFS-TL characteristic to network state and improve detection performance. Finally, we carried out extensive experiments on several network traffic datasets for anomaly detection, and the results demonstrate the superiority of IFSE-AD to state-of-the-art approaches, validating the effectiveness of our method.INDEX TERMS network traffic flow, flow interaction, complex network, anomaly detection, multivariate flow similarity, temporal locality, intuitionistic fuzzy set, ensemble method.

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“…Our WDPVG approach considers uneven sampling effects, and simultaneously captures peaks and troughs of time series. Previously, VG edge weights have been assigned based on the arctangent of ((s j − s i )/(t j − t i )), which computes the "view angle" along the direct line-of-sight connecting one intensity peak to another (Supriya et al, 2016). Our method provides multiple choices for the edge weights: (i) the Euclidean distance between nodes/intensity peaks, (ii) the tangent of the view angle between two nodes, (iii)the time difference between time points corresponding to connected nodes, or (iv) none.…”

Section: Introductionmentioning

confidence: 99%

“…cutoffs/thresholds) (Liu et al, 2015). VGs have been widely applied in many fields (Supriya et al, 2016;Stephen et al, 2015;Bezsudnov and Snarskii, 2014). However, as we discussed above, the VG has two disadvantages: first, it does not consider the effect of uneven sampling; second, it cannot capture the time series changes below a zero baseline.…”

Section: Introductionmentioning

confidence: 99%

Visibility Graph Based Community Detection for Biological Time Series

Min

Domanskyi

Piermarocchi

et al. 2020

Preprint

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MotivationTemporal behavior is an essential aspect of all biological systems. Time series have been previously represented as networks. Such representations must address two fundamental problems: (i) How to create the appropriate network to reflect the characteristics of biological time series. (ii) How to detect characteristic temporal patterns or events as network communities. General methods to detect communities have used metrics to compare the connectivity within a community to the connectivity one would expect in a random model, or assumed a known number of communities, or are based on the betweenness centrality of edges or nodes. However, such methods were not specifically designed for network representations of time series. We introduce a visibility-graph-based method to build networks from different kinds of biological time series and detect temporal communities within these networks.ResultsTo characterize the uneven sampling of typical experimentally obtained biological time series, and simultaneously capture events associated to peaks and troughs, we introduce the Weighted Dual-Perspective Visibility Graph (WDPVG) for time series. To detect communities, we first find the shortest path of the network between start and end nodes to identify nodes which have high intensities. This identifies the main stem of our community detection algorithm. Then, we aggregate nodes outside the shortest path to the nodes found on the main stem based on the closest path length. Through simulation, we demonstrate the validity of our method in detecting community structures on various networks derived from simulated time series. We also confirm its effectiveness in revealing temporal communities in experimental biological time series. Our results suggest our method of visibility graph based community detection can be effective in detecting temporal biological patterns.AvailabilityThe methods of building WDPVG and visibility graph based community detection are available as a module of the open source Python package PyIOmica (https://doi.org/10.5281/zenodo.3691912) with documentation at https://pyiomica.readthedocs.io/en/latest/. The dataset and codes we used in this manuscript are publicly available at https://doi.org/10.5281/zenodo.3693984.Contactgmias@msu.edu

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Weighted Visibility Graph With Complex Network Features in the Detection of Epilepsy

Cited by 166 publications

References 32 publications

Visibility graph analysis of wall turbulence time-series

Visibility graph analysis of wall turbulence time-series

Using Intuitionistic Fuzzy Set for Anomaly Detection of Network Traffic From Flow Interaction

Visibility Graph Based Community Detection for Biological Time Series

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