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Visualisation of Structure and Processes on Temporal Networks

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Temporal Network Theory

Part of the book series: Computational Social Sciences ((CSS))

Abstract

The temporal dimension increases the complexity of network models but also provides more detailed information about the sequence of connections between nodes allowing a more detailed mapping of processes taking place on the network. The visualisation of such evolving structures thus permits faster identification of non-trivial activity patterns and provides insights about the mechanisms driving the dynamics on and of networks. In this chapter, we introduce key concepts and discuss visualisation methods of temporal networks based on 2D layouts where nodes correspond to horizontal lines with circles to represent active nodes and vertical edges connecting those active nodes at given times. We introduce and discuss algorithms to re-arrange nodes and edges to reduce visual clutter, layouts to highlight node and edge activity, and visualise dynamic processes on temporal networks. We illustrate the methods using real-world temporal network data of face-to-face human contacts and simulated random walk and infection dynamics.

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Notes

  1. 1.

    Art-conscious researchers may also call for aesthetic visualisations.

  2. 2.

    To our knowledge, the oldest available online record suggests that the term ridiculogram was coined by Marc Vidal as early as 2007 (www.cs.unm.edu/~aaron/blog/archives/2007/05/ipam_random_and.htm).

  3. 3.

    The snapshot τ coincides with time t if δ = 1.

  4. 4.

    Somewhat similar to the force-directed graph drawing algorithm for the structural layout [9], except that in our case nodes’ positions are fixed.

  5. 5.

    This method is similar to heatmap grids [37].

  6. 6.

    We use the notation of snapshots τ rather than time t to emphasise that measures take into account snapshots, see Sect. 2 for definitions.

  7. 7.

    In this particular face-to-face experiment, badges were not allowed outside the school.

  8. 8.

    Available at https://github.com/jgraph/jgraphx. No need of separate installation to run DyNetVis.

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Author information

Authors and Affiliations

  1. Faculty of Computing, Federal University of Uberlândia, Uberlândia, Brazil

    Claudio D. G. Linhares, Jean R. Ponciano, Jose Gustavo S. Paiva & Bruno A. N. Travençolo

  2. Department of General Economics, Ghent University, Ghent, Belgium

    Luis E. C. Rocha

  3. Department of International Business and Economics, University of Greenwich, London, UK

    Luis E. C. Rocha

Authors
  1. Claudio D. G. Linhares
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  2. Jean R. Ponciano
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  3. Jose Gustavo S. Paiva
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  4. Bruno A. N. Travençolo
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  5. Luis E. C. Rocha
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Corresponding author

Correspondence to Luis E. C. Rocha .

Editor information

Editors and Affiliations

  1. Tokyo Tech World Research Hub Initiative (WRHI), Institute of Innovative Research, Tokyo Institute of Technology, Tokyo, Japan

    Petter Holme

  2. Department of Computer Science, Aalto University, Espoo, Finland

    Jari Saramäki

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Linhares, C.D.G., Ponciano, J.R., Paiva, J.G.S., Travençolo, B.A.N., Rocha, L.E.C. (2019). Visualisation of Structure and Processes on Temporal Networks. In: Holme, P., Saramäki, J. (eds) Temporal Network Theory. Computational Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-030-23495-9_5

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