Pixel-Oriented Visualization of Change in Social Networks

Stein, Klaus; Wegener, René; Schlieder, Christoph

doi:10.1109/asonam.2010.18

Cited by 35 publications

(22 citation statements)

References 15 publications

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“…Figure 2 shows an example of network visualization by either the node-link diagram or the adjacency matrix. [10]. Also, Brandes and Nick introduced gestaltlines, a new expression of matrix cells [11].…”

Section: Matrix Vs Node-link Diagrammentioning

confidence: 95%

BLOCKS: Efficient and Stable Online Visualization of Dynamic Network Evolution

Ohsawa

2016

Rev Socionetwork Strat

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It is computationally and cognitively expensive to observe the evolution of community structure on dynamic networks in real-time, not only because the data sets tend to be complex, but also because the visual interfaces are often complicated. We introduce BLOCKS, a simple but efficient framework to abstract and visualize the evolution of community structure on dynamic networks. Instead of indicating detailed changes of nodes and links temporally, BLOCKS regards communities as visual entities and focuses on representing their behaviors and relation changes on a time series. Experiments detected a stable performance of BLOCKS compared with previous methods while detecting the community structure of networks. We also present a case study that shows an effective learning process of network evolution with BLOCKS.

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Section: Matrix Vs Node-link Diagrammentioning

confidence: 95%

BLOCKS: Efficient and Stable Online Visualization of Dynamic Network Evolution

Ohsawa

2016

Rev Socionetwork Strat

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“…This concept allows visually comparing several subsequent graphs at once, but requires the same simple nodelayout in all graphs. But time-to-space mappings can also be applied to matrix representations: Each cell of the matrix contains a small timeline representing the evolution of a particular edge-various concepts have been proposed for these miniature timelines [15], [16].…”

Section: Exemplary Visualization Profilesmentioning

confidence: 99%

“…Pixel-Oriented Matrix (POM) [16] A static (time-to-space mapping) matrix representation. In an adjacency matrix, each cell represents an edge as a combination of two nodes.…”

Section: Exemplary Visualization Profilesmentioning

confidence: 99%

Matching Application Requirements with Dynamic Graph Visualization Profiles

Beck

Burch

Diehl

2013

2013 17th International Conference on Information Visualisation

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Mapping a dynamic graph dataset to an inappropriate visualization leads to a degradation of visualization performance at some task. To tap the full potential of existing dynamic graph visualization techniques, we propose a methodology for matching application requirements with dynamic graph visualization profiles. We target at supporting experts choosing the right visualization technique. Our methodology describes both the application requirements and the visualization techniques as profiles covering important aesthetic criteria for visualizing dynamic graphs. Characteristics of the graph and task are used to derive the application profile. The probably most appropriate visualization technique is the one whose profile matches best the required application profile. We compile exemplary visualization profiles for representatives of dynamic graph visualization approaches and demonstrate the methodology in a case study.

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“…In contrast, recent timelinebased approaches support the comparison of edges across larger time spans: For instance, the Parallel Edge Splatting technique [17] uses juxtaposed node-link diagrams and can be considered as a variant of PNLV. But also matrices are used in this context such as the Pixel Oriented Matrix [28], which splits matrix cells into sub-cells similar to IMMV.…”

Section: Related Workmentioning

confidence: 99%

Finding structures in multi-type code couplings with node-link and matrix visualizations

Abuthawabeh

Beck

Zeckzer

et al. 2013

2013 First IEEE Working Conference on Software Visualization (VISSOFT)

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Software systems are often modeled and visualized as graphs in order to understand their higher-level structure: code entities are connected by dependencies or couplings. However, when only considering one type of code coupling such as method calls, the understanding gained stays limited to this specific aspect. Encoding multiple types of code coupling in the graph promises to broaden the understanding. Different approaches already exist for visually discerning those types in graph diagrams. In this paper, we study two of these techniquesa node-link and a matrix approach-in a realistic scenario where the classes and interfaces of a system are connected by six different types of code coupling. The explorative user study that we conducted with interactive versions of the two visualizations focuses on getting an insight on how software developers use the visualizations for understanding an unknown system. We classified typical visual structures that the participants were able to identify and connected these structures to software engineering problems. Despite the fundamental difference in approach, the participants identified the same graph structures targeting similar tasks with both visualizations.

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Pixel-Oriented Visualization of Change in Social Networks

Cited by 35 publications

References 15 publications

BLOCKS: Efficient and Stable Online Visualization of Dynamic Network Evolution

BLOCKS: Efficient and Stable Online Visualization of Dynamic Network Evolution

Matching Application Requirements with Dynamic Graph Visualization Profiles

Finding structures in multi-type code couplings with node-link and matrix visualizations

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