Text size
  • Small
  • Medium
  • Large
Contrast
  • Standard
  • Blue text on blue
  • High contrast (Yellow text on black)
  • Blue text on beige

    Data Network Simulator with Classical Ballet

    Electronic Visualisation and the Arts (EVA 2016)

    London, UK, 12 - 14 July 2016

    AUTHORS

    Genevieve Smith-Nunes, Peter Cook, Camilla Neale & Paul Golz

    ABSTRACT

    http://dx.doi.org/10.14236/ewic/EVA2016.13

    [data]storm, from readysaltedcode CIC, a data driven dance performance. The development of a social network simulator to demonstrate network growth and message propagation. The underpinning theory of piece stems from social network theory (SNT), graph theory, computer mediated communication (CMC) through to social information processing (SIP) and Computational Thinking (CT). The data visualisation is linked to the physical ballet movements of the dancers, they are a manifestation of the data. The data visualisations on screen link to the live dancers’ performance patterns and modify to create the visuals and movements of data transmission across a network.

    Network growth. The first of the simulations shows network growth. Each node in the network represents a user who has the following characteristics:

    • friendliness (how often they are likely to make friends with another user)
    • chattiness (how often they send out messages)
    • category (the subject area in which they are most interested)

    At random time intervals things occur: New users are added to the network depending on the above characteristics, users become friends with each other. All the rules stay the same throughout the simulation.

    At the same time the dance (ballet) movements and wearables (LEDS) were choreographed/coded to accompany the data visualisation using network mapping techniques. The choreography and wearables elements link to the friendliness and chattiness of each of the nodes in the simulated network. This network simulation is further utilised in the Virus section of the performance using the same rules to simulate how a virus can spread through a network. Further work on this simulation will look at two things 1. Message propagation and viral messaging within a social network like Twitter. 2. Pain signals within the body and how they compare to data transfer within a social network.

    PAPER FORMATS

    PDF file PDF Version of this Paper 548(kb)