ChordLink: A New Hybrid Visualization Model
Many real-world networks are globally sparse but locally dense. Typical examples are social networks, biological networks, and information networks. This double structural nature makes it difficult to adopt a homogeneous visualization model that clearly conveys an overview of the network and the int...
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Published in | Graph Drawing and Network Visualization Vol. 11904; pp. 276 - 290 |
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Main Authors | , , , , |
Format | Book Chapter |
Language | English |
Published |
Switzerland
Springer International Publishing AG
2019
Springer International Publishing |
Series | Lecture Notes in Computer Science |
Online Access | Get full text |
ISBN | 3030358011 9783030358013 |
ISSN | 0302-9743 1611-3349 |
DOI | 10.1007/978-3-030-35802-0_22 |
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Summary: | Many real-world networks are globally sparse but locally dense. Typical examples are social networks, biological networks, and information networks. This double structural nature makes it difficult to adopt a homogeneous visualization model that clearly conveys an overview of the network and the internal structure of its communities at the same time. As a consequence, the use of hybrid visualizations has been proposed. For instance, NodeTrix combines node-link and matrix-based representations (Henry et al., 2007). In this paper we describe ChordLink, a hybrid visualization model that embeds chord diagrams, used to represent dense subgraphs, into a node-link diagram, which shows the global network structure. The visualization is intuitive and makes it possible to interactively highlight the structure of a community while keeping the rest of the layout stable. We discuss the intriguing algorithmic challenges behind the ChordLink model, present a prototype system, and illustrate case studies on real-world networks. |
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Bibliography: | Work partially supported by: (i) MIUR, under grant 20174LF3T8 “AHeAD: efficient Algorithms for HArnessing networked Data”, (ii) Dipartimento di Ingegneria - Università degli Studi di Perugia, under grants RICBASE2017WD and RICBA18WD: “Algoritmi e sistemi di analisi visuale di reti complesse e di grandi dimensioni”. |
ISBN: | 3030358011 9783030358013 |
ISSN: | 0302-9743 1611-3349 |
DOI: | 10.1007/978-3-030-35802-0_22 |