Reducing Snapshots to Points: A Visual Analytics Approach to Dynamic Network Exploration

Elzen, Stef van den; Holten, Danny; Blaas, Jorik; Wijk, Jarke J. van

doi:10.1109/tvcg.2015.2468078

Cited by 142 publications

(119 citation statements)

References 44 publications

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“…The central histological findings in non-syndromic ATAA analyses are the loss of smooth muscle cells (media degeneration) and the alteration of elastic fibre structures [11, 12]. Importantly, older but also very new results suggest that even these two “hallmarks” of ATAA pathogenesis may not occur generally, and (non-syndromic) ATAA pathogenesis concepts may have to be revisited [13, 14].…”

Section: Introductionmentioning

confidence: 99%

Metabolomic profiling of ascending thoracic aortic aneurysms and dissections - Implications for pathophysiology and biomarker discovery

et al. 2017

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ObjectiveOur basic understanding of ascending thoracic aortic aneurysm (ATAA) pathogenesis is still very limited, hampering early diagnosis, risk prediction, and development of treatment options. “Omics”-technologies, ideal to reveal tissue alterations from the normal physiological state due to disease have hardly been applied in the field. Using a metabolomic approach, with this study the authors seek to define tissue differences between controls and various forms of ATAAs.MethodsUsing a targeted FIA-MS/MS metabolomics approach, we analysed and compared the metabolic profiles of ascending thoracic aortic wall tissue of age-matched controls (n = 8), bicuspid aortic valve-associated aneurysms (BAV-A; n = 9), tricuspid aortic valve-associated aneurysms (TAV-A; n = 14), and tricuspid aortic valve-associated aortic dissections (TAV-Diss; n = 6).ResultsWith sphingomyelin (SM) (OH) C22:2, SM C18:1, SM C22:1, and SM C24:1 only 4 out of 92 detectable metabolites differed significantly between controls and BAV-A samples. Between controls and TAV-Diss samples only phosphatidylcholine (PC) ae C32:1 differed. Importantly, our analyses revealed a general increase in the amount of total sphingomyelin levels in BAV-A and TAV-Diss samples compared to controls.ConclusionsSignificantly increased levels of sphingomyelins in BAV-A and TAV-Diss samples compared to controls may argue for a repression of sphingomyelinase activity and the sphingomyelinase-ceramide pathway, which may result in an inhibition of tissue regeneration; a potential basis for disease initiation and progression.

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Section: Introductionmentioning

confidence: 99%

Metabolomic profiling of ascending thoracic aortic aneurysms and dissections - Implications for pathophysiology and biomarker discovery

et al. 2017

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“…Alternatively to time clustering, the vectors representing spatial situations at different time steps can be projected onto a plane, as proposed recently for generic (non-spatial) graphs [14] and other types of time-variant data [15]. Each situation ("snapshot" [14]) is represented by a point on the plane.…”

Section: 2mentioning

confidence: 99%

“…Each situation ("snapshot" [14]) is represented by a point on the plane. Points corresponding to similar situations tend to form clusters.…”

Section: 2mentioning

confidence: 99%

Revealing Patterns and Trends of Mass Mobility Through Spatial and Temporal Abstraction of Origin-Destination Movement Data

Andrienko

Fuchs

et al. 2017

IEEE Trans. Visual. Comput. Graphics

101

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract-Origin-destination (OD) movement data describe moves or trips between spatial locations by specifying the origins, destinations, start, and end times, but not the routes travelled. For studying the spatio-temporal patterns and trends of mass mobility, individual OD moves of many people are aggregated into flows (collective moves) by time intervals. Time-variant flow data pose two difficult challenges for visualization and analysis. First, flows may connect arbitrary locations (not only neighbors), thus making a graph with numerous edge intersections, which is hard to visualize in a comprehensible way. Even a single spatial situation consisting of flows in one time step is hard to explore. The second challenge is the need to analyze long time series consisting of numerous spatial situations. We present an approach facilitating exploration of long-term flow data by means of spatial and temporal abstraction. It involves a special way of data aggregation, which allows representing spatial situations by diagram maps instead of flow maps, thus reducing the intersections and occlusions pertaining to flow maps. The aggregated data are used for clustering of time intervals by similarity of the spatial situations. Temporal and spatial displays of the clustering results facilitate the discovery of periodic patterns and longer-term trends in the mass mobility behavior. Permanent repository link

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“…The limitations of this method includes a requirement for more screen space and a difficulty to compare snapshots. Other methods to enhance visual pattern detection in dynamic networks include employing 'piling' metaphors [3], adjacency lists, or representing network snapshots as high-dimensional points that are projected in two dimensions [20]. There are also strategies as Temporal Trends [4] and Heatmap Grids [9] that are used to perform visual analytics of firewall log events and dense networks in astronomy and neurology, respectively.…”

Section: Related Workmentioning

confidence: 99%

DyNetVis

Linhares

Travençolo

Paiva

et al. 2017

Proceedings of the Symposium on Applied Computing

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The concept of networks has been important in the study of complex systems. In networks, links connect pairs of nodes forming complex structures. Studies have shown that networks not only contain structure but may also evolve in time. The addition of the temporal dimension adds complexity on the analysis and requests the development of innovative methods for the visualization of real-life networks. In this paper we introduce the Dynamic Network Visualization System (DyNetVis), a software tool for visualization of dynamic networks. The system provides several tools for user interaction and offers two coordinated visual layouts, named structural and temporal. Structural refers to standard network drawing techniques, in which a single snapshot of nodes and links are placed in a plane, whereas the temporal layout allows for simultaneously visualization of several temporal snapshots of the dynamic network. In addition, we also investigate two approaches for temporal layout visualization: (i) Recurrent Neighbors, a node ordering strategy that highlights frequent connections in time, and (ii) Temporal Activity Map (TAM), a layout technique with focus on nodes activity. We illustrate the applicability of the layouts and interaction functionalities provided by the system in two visual analysis case studies, demonstrating their advantages to improve the overall user experience on visualization and exploratory data analysis on dynamic networks.

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Reducing Snapshots to Points: A Visual Analytics Approach to Dynamic Network Exploration

Cited by 142 publications

References 44 publications

Metabolomic profiling of ascending thoracic aortic aneurysms and dissections - Implications for pathophysiology and biomarker discovery

Metabolomic profiling of ascending thoracic aortic aneurysms and dissections - Implications for pathophysiology and biomarker discovery

Revealing Patterns and Trends of Mass Mobility Through Spatial and Temporal Abstraction of Origin-Destination Movement Data

DyNetVis

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