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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Bibliographic Details
Published inGraph Drawing and Network Visualization Vol. 11904; pp. 276 - 290
Main Authors Angori, Lorenzo, Didimo, Walter, Montecchiani, Fabrizio, Pagliuca, Daniele, Tappini, Alessandra
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2019
Springer International Publishing
SeriesLecture Notes in Computer Science
Online AccessGet full text
ISBN3030358011
9783030358013
ISSN0302-9743
1611-3349
DOI10.1007/978-3-030-35802-0_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.
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