Evaluating overall quality of graph visualizations based on aesthetics aggregation

Huang, Weidong; Huang, Mao Lin; Lin, Chun‐Cheng

doi:10.1016/j.ins.2015.05.028

Cited by 31 publications

(11 citation statements)

References 24 publications

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“…Examples of such metrics are, inter alia, number of edge 1 crossings, number of edge bends, and angular resolution (refer to Figure 1 ). The connection of those metrics to user performance has been studied empirically (e.g., Huang, Hong, & Eades, 2006 ; Huang, Huang, & Lin, 2016 ; Purchase, 1997 ; Purchase, Hamer, Nollenburg, & Kobourov, 2012 ; Ware, Purchase, Colpoys, & McGill, 2002 ). Node-link diagrams have been empirically studied from the perspective of enjoyment ( Saket, Scheidegger, & Kobourov, 2016 ) and subjective usability prospects ( Huang et al., 2006 ; Purchase, Allder, & Carrington, 2002 ; Purchase, Hamer, et al., 2012 ).…”

Section: Introductionmentioning

confidence: 99%

The Power of Shape: How Shape of Node-Link Diagrams Impacts Aesthetic Appreciation and Triggers Interest

et al. 2018

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Positive effects of aesthetically appreciated designs have long been studied and confirmed since the 19th century: such designs are more enjoyable, they are more forgivable for glitches and can increase users’ performance. In the field of information visualization, studies of aesthetics are still a niche approach. In the current study, we aim to specifically understand which parameters in a visualization of node-link diagrams make them aesthetically pleasing-an important extension to already existing research on usability and readability aspects. We investigated how the shape of the outline of such diagrams influences the aesthetic judgments on two of the most important dimensions of aesthetic appeal: beauty and interest. We employed different outlines to node-link diagrams and compared them with uniformly filled shapes, varying two important variables typically impacting aesthetics: complexity and curvature. This was done for a short (100 ms) and ad libitum presentation time. Diagrams with curvier outlines were perceived as more beautiful, while diagrams with more complex outlines were considered to be more interesting. These dependencies already exist for presaccadic perception (100 ms) and are slightly stronger for unlimited presentation time. We also found that curvature is a predictor for beauty only for unlimited presentation time. Aesthetic appeal was very similar for diagrams and pure shapes, so many results from fundamental research on aesthetics can potentially be transferred to the community of network visualization, assisting to improve visualizations also in aesthetic regards.

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Section: Introductionmentioning

confidence: 99%

The Power of Shape: How Shape of Node-Link Diagrams Impacts Aesthetic Appreciation and Triggers Interest

et al. 2018

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“…Often, they are too large and complex to understand in their original format. This type of data can be modelled as graphs or networks and visualised into node-link diagrams for a better understanding of them [10,[13][14][15]. In graph visualisation research, the focus has been on how the elements are connected as a system, not just individual elements [16].…”

Section: Graph Visualisationmentioning

confidence: 99%

“…For example, the weight of the edge that connects standard 12207:2008 and 9126:1991 is 2, since one standard is normative and the other one is informative. Final graph models are generated for further experiments following the GraphML (Graph Mark-up Language) format, which is an XML-based format for the description of graph structures, designed to improve tool interoperability and reduce communication overhead [13]. An XML graph file sample is given in Appendix A that also represents a graph shown in Figure 3.…”

mentioning

confidence: 99%

Applying Graph Centrality Metrics in Visual Analytics of Scientific Standard Datasets

Hua

Huang

et al. 2019

Symmetry

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Graphs are often used to model data with a relational structure and graphs are usually visualised into node-link diagrams for a better understanding of the underlying data. Node-link diagrams represent not only data entries in a graph, but also the relations among the data entries. Further, many graph drawing algorithms and graph centrality metrics have been successfully applied in visual analytics of various graph datasets, yet little attention has been paid to analytics of scientific standard data. This study attempts to adopt graph drawing methods (force-directed algorithms) to visualise scientific standard data and provide information with importance ‘ranking’ based on graph centrality metrics such as Weighted Degree, PageRank, Eigenvector, Betweenness and Closeness factors. The outcomes show that our method can produce clear graph layouts of scientific standard for visual analytics, along with the importance ‘ranking’ factors (represent via node colour, size etc.). Our method may assist users with tracking various relationships while understanding scientific standards with fewer relation issues (missing/wrong connection etc.) through focusing on higher priority standards.

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“…In another work, Huang et al [15] have empirically demonstrated that their "aggregate" metric is sensitive to quality changes and is correlated with the human performance in graph comprehension tasks. They have also noticed that the dependence of aesthetic quality on input quality metrics can be non-linear (e.g.…”

Section: Related Workmentioning

confidence: 99%

“…In order to assess the relative standing of the suggested method, we have implemented two known aesthetic metrics (stress and the combined metric by Huang et al [15]) and evaluated them over the same dataset. The metric values were trivially converted to the respective discriminator function outputs.…”

Section: Comparison With Other Metricsmentioning

confidence: 99%

Aesthetic Discrimination of Graph Layouts

Klammler

Mchedlidze

Pak

2018

Lecture Notes in Computer Science

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This paper addresses the following basic question: given two layouts of the same graph, which one is more aesthetically pleasing? We propose a neural network-based discriminator model trained on a labeled dataset that decides which of two layouts has a higher aesthetic quality. The feature vectors used as inputs to the model are based on known graph drawing quality metrics, classical statistics, informationtheoretical quantities, and two-point statistics inspired by methods of condensed matter physics. The large corpus of layout pairs used for training and testing is constructed using force-directed drawing algorithms and the layouts that naturally stem from the process of graph generation. It is further extended using data augmentation techniques. Our model demonstrates a mean prediction accuracy of 96.48 %, outperforming discriminators based on stress and on the linear combination of popular quality metrics by a small but statistically significant margin.

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Evaluating overall quality of graph visualizations based on aesthetics aggregation

Cited by 31 publications

References 24 publications

The Power of Shape: How Shape of Node-Link Diagrams Impacts Aesthetic Appreciation and Triggers Interest

The Power of Shape: How Shape of Node-Link Diagrams Impacts Aesthetic Appreciation and Triggers Interest

Applying Graph Centrality Metrics in Visual Analytics of Scientific Standard Datasets

Aesthetic Discrimination of Graph Layouts

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