Exploring the limits of complexity: A survey of empirical studies on graph visualisation

Yoghourdjian, Vahan; Archambault, Daniel; Diehl, Stephan; Dwyer, Tim; Klein, Karsten; Purchase, Helen C.; Wu, Hsiang‐Yun

doi:10.1016/j.visinf.2018.12.006

Cited by 74 publications

(60 citation statements)

References 135 publications

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“…Similarly, | V D |,| E D |, and density ( Den D ) are the respective values after node duplication. The edge density function for Den and Den D is defined as | E |/(| V | 2 −| V |) [33] in Table 1. Note that here we decompose a hyperedge of a reaction into multiple edge segments used to connect metabolites to construct our network.…”

Section: Experimental Results Evaluation and Discussionmentioning

confidence: 99%

“…Interactions such as semantic zooming [30] and aggregation [31, 32] have been investigated to analyze large networks. Although interactions have been important tools to facilitate users’ capabilities to understand large datasets, it has also been studied that interactive activities during the analysis process may increase time for accomplishing simple connectivity tasks [33]. Nevertheless, interaction is definitely a valuable way to support analytical processes, where users can expand and collapse the visualization to retrieve their target of interests.…”

Section: Related Workmentioning

confidence: 99%

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Metabopolis: scalable network layout for biological pathway diagrams in urban map style

Wu¹,

Nöllenburg

Sousa

et al. 2019

BMC Bioinformatics

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Background Biological pathways represent chains of molecular interactions in biological systems that jointly form complex dynamic networks. The network structure changes from the significance of biological experiments and layout algorithms often sacrifice low-level details to maintain high-level information, which complicates the entire image to large biochemical systems such as human metabolic pathways. Results Our work is inspired by concepts from urban planning since we create a visual hierarchy of biological pathways, which is analogous to city blocks and grid-like road networks in an urban area. We automatize the manual drawing process of biologists by first partitioning the map domain into multiple sub-blocks, and then building the corresponding pathways by routing edges schematically, to maintain the global and local context simultaneously. Our system incorporates constrained floor-planning and network-flow algorithms to optimize the layout of sub-blocks and to distribute the edge density along the map domain. We have developed the approach in close collaboration with domain experts and present their feedback on the pathway diagrams based on selected use cases. Conclusions We present a new approach for computing biological pathway maps that untangles visual clutter by decomposing large networks into semantic sub-networks and bundling long edges to create space for presenting relationships systematically. Electronic supplementary material The online version of this article (10.1186/s12859-019-2779-4) contains supplementary material, which is available to authorized users.

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Section: Experimental Results Evaluation and Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Metabopolis: scalable network layout for biological pathway diagrams in urban map style

Wu¹,

Nöllenburg

Sousa

et al. 2019

BMC Bioinformatics

Self Cite

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“…Since Tutte [72,73] proposed his barycenter method for graph drawing more than fifty years ago, the information visualization community has proposed many graph drawing techniques. These algorithms can be found in various books [4,49,71] and surveys [16,30,41,74,79]. Typically, graph drawing algorithms generate only one drawing for a graph, though some work [6] also proposes producing multiple drawings for the same graph.…”

Section: Graph Drawingmentioning

confidence: 99%

DeepDrawing: A Deep Learning Approach to Graph Drawing

Wang

Jin

Wang

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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Node-link diagrams are widely used to facilitate network explorations. However, when using a graph drawing technique to visualize networks, users often need to tune different algorithm-specific parameters iteratively by comparing the corresponding drawing results in order to achieve a desired visual effect. This trial and error process is often tedious and time-consuming, especially for non-expert users. Inspired by the powerful data modelling and prediction capabilities of deep learning techniques, we explore the possibility of applying deep learning techniques to graph drawing. Specifically, we propose using a graph-LSTM-based approach to directly map network structures to graph drawings. Given a set of layout examples as the training dataset, we train the proposed graph-LSTM-based model to capture their layout characteristics. Then, the trained model is used to generate graph drawings in a similar style for new networks. We evaluated the proposed approach on two special types of layouts (i.e., grid layouts and star layouts) and two general types of layouts (i.e., ForceAtlas2 and PivotMDS) in both qualitative and quantitative ways. The results provide support for the effectiveness of our approach. We also conducted a time cost assessment on the drawings of small graphs with 20 to 50 nodes. We further report the lessons we learned and discuss the limitations and future work.

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“…For a detailed survey on network complexity and empirical evaluations, the reader is referred to the work of Yoghourdjian et al . [YAD*18].…”

Section: Multilayer Network and Related Conceptsmentioning

confidence: 99%

The State of the Art in Multilayer Network Visualization

McGee

Ghoniem

Mélançon

et al. 2019

Computer Graphics Forum

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Modelling relationship between entities in real‐world systems with a simple graph is a standard approach. However, reality is better embraced as several interdependent subsystems (or layers). Recently, the concept of a multilayer network model has emerged from the field of complex systems. This model can be applied to a wide range of real‐world data sets. Examples of multilayer networks can be found in the domains of life sciences, sociology, digital humanities and more. Within the domain of graph visualization, there are many systems which visualize data sets having many characteristics of multilayer graphs. This report provides a state of the art and a structured analysis of contemporary multilayer network visualization, not only for researchers in visualization, but also for those who aim to visualize multilayer networks in the domain of complex systems, as well as those developing systems across application domains. We have explored the visualization literature to survey visualization techniques suitable for multilayer graph visualization, as well as tools, tasks and analytic techniques from within application domains. This report also identifies the outstanding challenges for multilayer graph visualization and suggests future research directions for addressing them.

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Exploring the limits of complexity: A survey of empirical studies on graph visualisation

Cited by 74 publications

References 135 publications

Metabopolis: scalable network layout for biological pathway diagrams in urban map style

Metabopolis: scalable network layout for biological pathway diagrams in urban map style

DeepDrawing: A Deep Learning Approach to Graph Drawing

The State of the Art in Multilayer Network Visualization

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