ChordLink: A New Hybrid Visualization Model

Hybrid visualizations combine different metaphors into a single network layout, in order to help humans in finding the "right way" of displaying the different portions of the network, especially when it is globally sparse and locally dense. We investigate hybrid visualizations in two complementary directions: (i) On the one hand, we evaluate the effectiveness of different hybrid visualization models through a comparative user study; (ii) On the other hand, we estimate the usefulness of an interactive visualization that integrates all the considered hybrid models together. The results of our study provide some hints about the usefulness of the different hybrid visualizations for specific tasks of analysis and indicates that integrating different hybrid models into a single visualization may offer a valuable tool of analysis.

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“…-CHORDLINK (CL). The model proposed in [15], [57]. Nodes outside clusters and inter-cluster edges are drawn as in the NODELINK model.…”

Section: Visualization Modelsmentioning

confidence: 99%

Comparative Study and Evaluation of Hybrid Visualizations of Graphs

Giacomo

Didimo

Liotta

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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“…-ChordLink (CL). This model has been introduced in [9,10]. Nodes outside clusters and inter-cluster edges are drawn as in the NodeLink model.…”

Section: Visualization Modelsmentioning

confidence: 99%

A User Study on Hybrid Graph Visualizations

Giacomo¹,

Didimo²,

Fabrizio³

et al. 2021

Preprint

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Hybrid visualizations mix different metaphors in a single layout of a network. In particular, the popular NodeTrix model, introduced by Henry, Fekete, and McGuffin in 2007, combines node-link diagrams and matrix-based representations to support the analysis of real-world networks that are globally sparse but locally dense. That idea inspired a series of works, proposing variants or alternatives to NodeTrix. We present a user study that compares the classical node-link model and three hybrid visualization models designed to work on the same types of networks. The results of our study provide interesting indications about advantages/drawbacks of the considered models on performing classical tasks of analysis. At the same time, our experiment has some limitations and opens up to further research on the subject.

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“…• Node-Link Layout: The analyst can start from an automatic visualization of a relatively small portion of the taxpayer network. To this aim, a classical node-link layout of the network appears to be a natural and intuitive choice, which is preferred to other types of representations that are more suitable for larger and denser graphs (see, e.g., [60]- [63]).…”

Section: The Visualize Phasementioning

confidence: 99%

Combining Network Visualization and Data Mining for Tax Risk Assessment

et al. 2020

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This paper presents a novel approach, called MALDIVE, to support tax administrations in the tax risk assessment for discovering tax evasion and tax avoidance. MALDIVE relies on a network model describing several kinds of relationships among taxpayers. Our approach suitably combines various data mining and visual analytics methods to support public officers in identifying risky taxpayers. MALDIVE consists of a 4-step pipeline: (i) A social network is built from the taxpayers data and several features of this network are extracted by computing both classical social network indexes and domain-specific indexes; (ii) an initial set of risky taxpayers is identified by applying machine learning algorithms; (iii) the set of risky taxpayers is possibly enlarged by means of an information diffusion strategy and the output is shown to the analyst through a network visualization system; (iv) a visual inspection of the network is performed by the analyst in order to validate and refine the set of risky taxpayers. We discuss the effectiveness of the MALDIVE approach through both quantitative analyses and case studies performed on real data in collaboration with the Italian Revenue Agency. INDEX TERMS Tax risk assessment, tax evasion discovery, network visualization, data mining, human-computer interaction.

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ChordLink: A New Hybrid Visualization Model

Cited by 13 publications

References 39 publications

Comparative Study and Evaluation of Hybrid Visualizations of Graphs

Comparative Study and Evaluation of Hybrid Visualizations of Graphs

A User Study on Hybrid Graph Visualizations

Combining Network Visualization and Data Mining for Tax Risk Assessment

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