Evolutionary learning of graph layout constraints from examples

Masui, Toshiyuki

doi:10.1145/192426.192468

Cited by 24 publications

(22 citation statements)

References 12 publications

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“…Different methods have been proposed for spatial adaptation of information. For example Masui [41] uses adaptive methods for graph layouts in user interfaces.…”

Section: Interface Modification Methodsmentioning

confidence: 99%

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Adaptive user interfaces in complex supervisory tasks

Yen

Acay

2009

ISA Transactions

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“…Different methods have been proposed for spatial adaptation of information. For example Masui [41] uses adaptive methods for graph layouts in user interfaces.…”

Section: Interface Modification Methodsmentioning

confidence: 99%

“…We propose this method in order to create the mapping by sampling different possible interfaces. This approach has been applied before [16,41] but in a sense of dichotomy and adaptation was not continuous. Gervasio et al [26] has used past information without considering the importance of the context during the user evaluation.…”

Section: Inference Enginementioning

confidence: 99%

Adaptive user interfaces in complex supervisory tasks

Yen

Acay

2009

ISA Transactions

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“…For example, Biedl et al [5] have introduced the concept of multidrawing, which systematically produces many different layouts of the same graph. Some methods use an evolutionary algorithm [2,62,78] to optimize a layout based on a human-in-the-loop assessment. However, these methods require constant human intervention throughout the optimization process.…”

Section: Graph Visualizationmentioning

confidence: 99%

A Deep Generative Model for Graph Layout

Kwon

2020

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. Generative modeling of layouts for the Les Misérables character co-occurrence network [55]. We train our generative model to construct a 2D latent space by learning from a collection of example layouts (training samples). From the grid of generated samples, we can see the smooth transitions between the different layouts. This shows that our model is capable of learning and generalizing abstract concepts of graph layouts, not just memorizing the training samples. Users can use this sample grid of the latent space as a WYSIWYG interface to generate a layout they want, without either blindly tweaking parameters of layout methods or requiring expert knowledge of layout methods. The color mapping of the latent space represents the shape-based metric [23] of the generated samples. Throughout the paper, unless otherwise specified, the node color represents the hierarchical community structure of the graph [68,79], so readers can easily compare node locations in different layouts. An interactive demo is available in the supplementary material [1].Abstract-Different layouts can characterize different aspects of the same graph. Finding a "good" layout of a graph is thus an important task for graph visualization. In practice, users often visualize a graph in multiple layouts by using different methods and varying parameter settings until they find a layout that best suits the purpose of the visualization. However, this trial-and-error process is often haphazard and time-consuming. To provide users with an intuitive way to navigate the layout design space, we present a technique to systematically visualize a graph in diverse layouts using deep generative models. We design an encoder-decoder architecture to learn a model from a collection of example layouts, where the encoder represents training examples in a latent space and the decoder produces layouts from the latent space. In particular, we train the model to construct a two-dimensional latent space for users to easily explore and generate various layouts. We demonstrate our approach through quantitative and qualitative evaluations of the generated layouts. The results of our evaluations show that our model is capable of learning and generalizing abstract concepts of graph layouts, not just memorizing the training examples. In summary, this paper presents a fundamentally new approach to graph visualization where a machine learning model learns to visualize a graph from examples without manually-defined heuristics.Machine learning approaches to graph-structured data, such as social networks and biological networks, require an effective representation of the graph structure. Recently, many graph neural networks (GNNs) have been proposed for representation learning on graphs, such as graph convolutional networks [53], GraphSAGE [39], and graph isomorphism networks [84]. They have achieved state-of-the-art performance for many tasks, such as graph classification, node classification, and link prediction. We also use GNNs for learning the complex relationship between ...

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“…For example, one term might penalize label-label overlap, while another may penalize distance between a label and its anchor region. This energy minimization approach has been applied to many labeling problems, including map labeling [1,8,9], graph labeling [3,16,20], and diagram labeling [2,13]. The GADGET toolkit [11] provides support for defining new energy functions for general layout optimization problems.…”

Section: Introductionmentioning

confidence: 99%

“…Masui [20] and Gajos and Weld [12] learn energy functions for layout from pairs of good and bad layouts. Masui requires that these pairs be created by a user, and Gajos and Weld require users to compare a sequence of automatically-generated pairs.…”

Section: Introductionmentioning

confidence: 99%

Specifying label layout style by example

Vollick

Vogel

Agrawala

et al. 2007

Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology

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Creating high-quality label layouts in a particular visual style is a time-consuming process. Although automated labeling algorithms can aid the layout process, expert design knowledge is required to tune these algorithms so that they produce layouts which meet the designer's expectations. We propose a system which can learn a label layout style from a single example layout and then apply this style to new labeling problems. Because designers find it much easier to create example layouts than tune algorithmic parameters, our system provides a more natural workflow for graphic designers. We demonstrate that our system is capable of learning a variety of label layout styles from examples.

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Evolutionary learning of graph layout constraints from examples

Cited by 24 publications

References 12 publications

Adaptive user interfaces in complex supervisory tasks

Adaptive user interfaces in complex supervisory tasks

A Deep Generative Model for Graph Layout

Specifying label layout style by example

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