2009
DOI: 10.1109/tvcg.2009.181
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Smooth Graphs for Visual Exploration of Higher-Order State Transitions

Abstract: Fig. 1. A smooth graph representation of a labeled biological time-series. Each ring represents a state, and the edges between states visualize the state transitions. This graph uses smooth curves to explicitly visualize third order transitions, so that each curved edge represents a unique sequence of four successive states. The orange node is part of a selection set, and all transitions matching the current selection are highlighted in orange.Abstract-In this paper, we present a new visual way of exploring st… Show more

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Cited by 37 publications
(29 citation statements)
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“…HoN visualization is sporadic in the literature. Blaas et al [6] proposed to visualize higher-order transitions by connecting nodes using higher-order curves. By following a smooth curve from one end to the other, one can identify which nodes are associated with higher-order transitions and what are the orders of the nodes.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…HoN visualization is sporadic in the literature. Blaas et al [6] proposed to visualize higher-order transitions by connecting nodes using higher-order curves. By following a smooth curve from one end to the other, one can identify which nodes are associated with higher-order transitions and what are the orders of the nodes.…”
Section: Related Workmentioning
confidence: 99%
“…We display the previous ports as circles to the left side of the higher-order nodes, as shown in Figure 3 (b). For each higher-order node, we draw a smooth high-order CatmullRom spline to connect its corresponding ports in the visit order for clear observation, as suggested by Blaas et al [6]. The curves exhibit color transition from red to blue, indicating the visit order of ports (i.e., red indicates the port visited first and blue indicates the current port).…”
Section: Dependency Viewmentioning
confidence: 99%
“…Spherical coordinates have been applied to show common behavioural cycles [14] and extended to show them in relation to other sensor channels by combining with parallel coordinates [36]. Further attributes have been derived to abstract from the raw sensor values, including animal movement via reconstructed pseudo-tracks using a dead reckoning approach [37], and labelled behaviour to show higher-order state transitions between behaviours [5]. Unlike previous work which assists in understanding and exploring sensor data, we introduce a system for the analysis of animal behaviour through classification.…”
Section: Visualization Of Tri-axial Datamentioning
confidence: 99%
“…Our work is also informed by contributions in the area of state space visualization, such as those made by Van Ham et al [30,31], Pretorius et al [24,23], and Blaas et al [5]. Recent work in this area explored the visualization of large state spaces, where traditional node-link diagrams become quickly over-cluttered and fail to support the analyst in generating insight.…”
Section: Graphs In Visual Analyticsmentioning
confidence: 99%