Egocentric storylines for visual analysis of large dynamic graphs

Muelder, Chris; Crnovrsanin, Tarik; Sallaberry, Arnaud; Ma, Kwan‐Liu

doi:10.1109/bigdata.2013.6691715

Cited by 11 publications

(5 citation statements)

References 35 publications

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“…Additional relevant vertices and connections are revealed only on demand, based on graph structure or specialized degree-of-interest functions that can incorporate semantic importance or users' interaction histories [72,55,33,45,102,26]. Recently, such approaches have been extended to dynamic graphs by incorporating temporal histories, and applying relevancy filtering to a storyline-based representation [75].…”

Section: Dynamic Graph Analyticsmentioning

confidence: 99%

Temporal Multivariate Networks

Archambault¹,

Abello²,

Kennedy³

et al. 2014

Lecture Notes in Computer Science

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Abstract. Networks that evolve over time, or dynamic graphs, have been of interest to the areas of information visualization and graph drawing for many years. Typically, its the structure of the dynamic graph that evolves as vertices and edges are added or removed from the graph. In a multivariate scenario, however, attributes play an important role and can also evolve over time. In this chapter, we characterize and survey methods for visualizing temporal multivariate networks. We also explore future applications and directions for this emerging area in the fields of information visualization and graph drawing.

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Section: Dynamic Graph Analyticsmentioning

confidence: 99%

Temporal Multivariate Networks

Archambault¹,

Abello²,

Kennedy³

et al. 2014

Lecture Notes in Computer Science

Self Cite

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“…Tanahashi and Ma [12] computed storyline visualizations automatically and showed how to adjust the geometry of individual lines to improve the aesthetics of their visualizations. Muelder et al [10] visualized clustered, dynamic graphs as storylines, summarizing the behavior of the local network surrounding user-selected foci.…”

Section: Introductionmentioning

confidence: 99%

Block Crossings in Storyline Visualizations

Dijk¹,

Fink²,

Fischer³

et al. 2017

JGAA

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Abstract. Storyline visualizations help visualize encounters of the characters in a story over time. Each character is represented by an xmonotone curve that goes from left to right. A meeting is represented by having the characters that participate in the meeting run close together for some time. In order to keep the visual complexity low, rather than just minimizing pairwise crossings of curves, we propose to count block crossings, that is, pairs of intersecting bundles of lines. Our main results are as follows. We show that minimizing the number of block crossings is NP-hard, and we develop, for meetings of bounded size, a constant-factor approximation. We also present two fixed-parameter algorithms and, for meetings of size 2, a greedy heuristic that we evaluate experimentally.

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“…Munroe [26] introduced the storyline visualization as hand-drawn illustrations in xkcd's "Movie Narrative Charts", where lines represent the characters of various popular movies and the scenes are ordered chronologically and represented by bundling the lines of the corresponding characters. This concept has been used to visualize various spatiotemporal data, like communities in time-varying graphs [25,33], software projects [28], topic analysis [9], etc.…”

Section: Introductionmentioning

confidence: 99%

Crossing Minimization in Storyline Visualization

Gronemann

Jünger

Liers

et al. 2016

Lecture Notes in Computer Science

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A storyline visualization is a layout that represents the temporal dynamics of social interactions along time by the convergence of chronological lines. Among the criteria oriented at improving aesthetics and legibility of a representation of this type, a small number of line crossings is the hardest to achieve. We model the crossing minimization in the storyline visualization problem as a multi-layer crossing minimization problem with tree constraints. Our algorithm can compute a layout with the minimum number of crossings of the chronological lines. Computational results demonstrate that it can solve instances with more than 100 interactions and with more than 100 chronological lines to optimality.

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Egocentric storylines for visual analysis of large dynamic graphs

Cited by 11 publications

References 35 publications

Temporal Multivariate Networks

Temporal Multivariate Networks

Block Crossings in Storyline Visualizations

Crossing Minimization in Storyline Visualization

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