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Network visualisations and the ‘so what?’ problem


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A provocation for the 'Network analysis and the cultural heritage sector' workshop in Luxembourg, 8 June 2016. Talk notes are available at

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Network visualisations and the ‘so what?’ problem

  1. 1. Network visualisations and the "so what?" problem Mia Ridge, @mia_out Digital Curator, British Library #BLdigital Expert Workshop Network Visualisation in the Cultural Heritage Sector 8 June 2016, Belval campus , University of Luxembourg
  2. 2. Caveat Not a critique of individual visualisations shown
  3. 3. Context
  4. 4. Provocation: digital humanists love network visualisations...
  5. 5. ...but ordinary people say, 'so what'?
  6. 6.
  7. 7. Location matters
  8. 8. Animated physics is ... pointless?
  9. 9. Size, weight, colour = meaning?
  10. 10. 'What does this tell me that I couldn't learn as quickly from a sentence, list or table?'
  11. 11. Which algorithmic choices are significant? Mike Bostock, force-directed and curved line graphs of character co-occurence in Les Misérables
  12. 12. Via @scott_bot
  13. 13. 'Can't see the wood for the trees'
  14. 14. Stories vs hairballs
  15. 15. No sense of change over time
  16. 16. No sense of texture, detail of sources
  17. 17. Jargon • Node • Edge • Graph
  18. 18. More jargon • Node • Edge • Graph • Directed, undirected • Betweenness • Closeness • Eccentricity
  19. 19. There is some hope...
  20. 20. Interactivity is engaging
  21. 21. ...but different users have different interaction needs
  22. 22. Proceed, with caution Working tool (exploration, process) vs public output (explanation, product)
  23. 23. But first - who are your 'users'?
  24. 24. Sometimes a network visualisation isn't the answer ... even if it was part of the question.
  25. 25. No more untethered images • Include an extended caption? – Data source, tools and algorithms used • Link to find out more? – Why this data, this form? – What was interesting but not easily visualised? – Download the dataset to explore yoursel?
  26. 26. Interesting stuff Cleaned data Data available to researchers All the data that could exist Visualisation! Iceberg idea HT Anne Baillot, Resisting networks
  27. 27. Talk about data that couldn't exist 'because we're only looking on one axis (letters), we get an inflated sense of the importance of spatial distance in early modern intellectual networks. Best friends never wrote to each other; they lived in the same city and drank in the same pubs; they could just meet on a sunny afternoon if they had anything important to say. Distant letters were important, but our networks obscure the equally important local scholarly communities.' Scott Weingart, 'Networks Demystified 8: When Networks are Inappropriate'
  28. 28. Help users learn the skills and knowledge they need to interpret network visualisations in context. How? Good question!
  29. 29. Over to you! Mia Ridge @mia_out Digital Curator, British Library #BLdigital