Visual Analysis of Multivariate State Transition Graphs

Pretorius, A.J.; Wijk, Jarke J. van

doi:10.1109/tvcg.2006.192

Cited by 44 publications

(31 citation statements)

References 12 publications

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“…Some techniques emphasize the layout of the graph based on the multivariate data, such as multivariate state transition graphs [38] and other techniques consider user interaction and navigation, including PivotGraph [30], which aggregates and places nodes on a grid of two dimensions of attributes specified by users; Semantic substrates [42], which organizes network nodes based on userdefined regions of attributes,, and GraphDice [3], which uses a plot matrix to present node-link plots for every combination of attributes of actors and edges in a social. Other tools abstract data attributes with network schemas to help users to explore data.…”

Section: Visualization Of Social Network With Attributesmentioning

confidence: 99%

SocialNetSense: Supporting sensemaking of social and structural features in networks with interactive visualization

Gou

Zhang

Luo

et al. 2012

2012 IEEE Conference on Visual Analytics Science and Technology (VAST)

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Increasingly, social network datasets contain social attribute information about actors and their relationship. Analyzing such network with social attributes requires making sense of not only its structural features, but also the relationship between social features in attributes and network structures. Existing social network analysis tools are usually weak in supporting complex analytical tasks involving both structural and social features, and often overlook users' needs for sensemaking tools that help to gather, synthesize, and organize information of these features. To address these challenges, we propose a sensemaking framework of social-network visual analytics in this paper. This framework considers both bottom-up processes, which are about constructing new understandings based on collected information, and top-down processes, which concern using prior knowledge to guide information collection, in analyzing social networks from both social and structural perspectives. The framework also emphasizes the externalization of sensemaking processes through interactive visualization. Guided by the framework, we develop a system, SocialNetSense, to support the sensemaking in visual analytics of social networks with social attributes. The example of using our system to analyze a scholar collaboration network shows that our approach can help users gain insight into social networks both structurally and socially, and enhance their process awareness in visual analytics.

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Section: Visualization Of Social Network With Attributesmentioning

confidence: 99%

SocialNetSense: Supporting sensemaking of social and structural features in networks with interactive visualization

Gou

Zhang

Luo

et al. 2012

2012 IEEE Conference on Visual Analytics Science and Technology (VAST)

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“…Wu et al [25] propose a layout approach to visualize multivariate networks on the surface of a sphere with a Self-Organizing Map. Pretorius and Wijk [26] visualize multivariate state transition graphs by hierarchical clustering based on a user-defined subset of node attributes. The clustering hierarchy is represented by a tree and the aggregated edges are shown as an arc diagram.…”

Section: 2mentioning

confidence: 99%

“…The interaction tools include multiscale and cross-scale views with network aggregation, node sector distortion, an ego-network view and a critical path view. The interactions provide functionalities to understand multiscale and cross-scale patterns of a network over a tree in comparison to previous tools [26][27][28][29][30]. Multiscale view.…”

Section: 2mentioning

confidence: 99%

TreeNetViz: Revealing Patterns of Networks over Tree Structures

Gou

Zhang²

2011

IEEE Trans. Visual. Comput. Graphics

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Abstract-Network data often contain important attributes from various dimensions such as social affiliations and areas of expertise in a social network. If such attributes exhibit a tree structure, visualizing a compound graph consisting of tree and network structures becomes complicated. How to visually reveal patterns of a network over a tree has not been fully studied. In this paper, we propose a compound graph model, TreeNet, to support visualization and analysis of a network at multiple levels of aggregation over a tree. We also present a visualization design, TreeNetViz, to offer the multiscale and cross-scale exploration and interaction of a TreeNet graph. TreeNetViz uses a Radial, Space-Filling (RSF) visualization to represent the tree structure, a circle layout with novel optimization to show aggregated networks derived from TreeNet, and an edge bundling technique to reduce visual complexity. Our circular layout algorithm reduces both total edge-crossings and edge length and also considers hierarchical structure constraints and edge weight in a TreeNet graph. These experiments illustrate that the algorithm can reduce visual cluttering in TreeNet graphs. Our case study also shows that TreeNetViz has the potential to support the analysis of a compound graph by revealing multiscale and cross-scale network patterns.

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“…Each state is represented by a vertex and each state change by a directed edge. State transition graphs are generally represented by simple node-link diagrams, where each node represents a state and each link a state transition [13].…”

Section: Related Workmentioning

confidence: 99%

“…Also related to this work are visualization techniques for biological sensor data including Ware et al [21] where accelerometer data was visualized using TrackPlots, which enabled scientists to check the theory that whales roll onto their sides for specific prey capture, and Grundy et al [6] where spherical plots, spherical overlays, spherical histograms and a posture state graph are shown to be effective at leading to biological understanding Pretorius et al [14,13] presented a unified approach to visualize highly complex state transition graphs, employing node-link diagrams to visualize a hierarchical clustering of the different states, the bar tree to visualize the number of occurrences of states and finally an arc diagram to visualize the actual state transitions. A method was also presented whereby multivariate graphs could be more explicitly explored: Nodes are arranged in a source group and a target group, and all edges are shown in between [15] and queries can be interactively performed.…”

Section: Related Workmentioning

confidence: 99%

Smooth Graphs for Visual Exploration of Higher-Order State Transitions

Blaas

Botha

Grundy

et al. 2009

IEEE Trans. Visual. Comput. Graphics

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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 state sequences in large observational time-series. A key advantage of our method is that it can directly visualize higher-order state transitions. A standard first order state transition is a sequence of two states that are linked by a transition. A higher-order state transition is a sequence of three or more states where the sequence of participating states are linked together by consecutive first order state transitions. Our method extends the current state-graph exploration methods by employing a two dimensional graph, in which higher-order state transitions are visualized as curved lines. All transitions are bundled into thick splines, so that the thickness of an edge represents the frequency of instances. The bundling between two states takes into account the state transitions before and after the transition. This is done in such a way that it forms a continuous representation in which any subsequence of the timeseries is represented by a continuous smooth line. The edge bundles in these graphs can be explored interactively through our incremental selection algorithm. We demonstrate our method with an application in exploring labeled time-series data from a biological survey, where a clustering has assigned a single label to the data at each time-point. In these sequences, a large number of cyclic patterns occur, which in turn are linked to specific activities. We demonstrate how our method helps to find these cycles, and how the interactive selection process helps to find and investigate activities.

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Visual Analysis of Multivariate State Transition Graphs

Cited by 44 publications

References 12 publications

SocialNetSense: Supporting sensemaking of social and structural features in networks with interactive visualization

SocialNetSense: Supporting sensemaking of social and structural features in networks with interactive visualization

TreeNetViz: Revealing Patterns of Networks over Tree Structures

Smooth Graphs for Visual Exploration of Higher-Order State Transitions

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