SlideShare wird heruntergeladen. ×
0
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Visualizing Networks: Beyond the Hairball
Nächste SlideShare
Wird geladen in ...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Visualizing Networks: Beyond the Hairball

36,486

Published on

Strata NYC 2012 slides by Lynn Cherny; layout and analysis strategies for network data.

Strata NYC 2012 slides by Lynn Cherny; layout and analysis strategies for network data.

Published in: Technologie
2 Kommentare
97 Gefällt mir
Statistiken
Notizen
Keine Downloads
Views
Gesamtviews
36,486
Bei Slideshare
0
Aus Einbettungen
0
Anzahl an Einbettungen
25
Aktionen
Geteilt
0
Downloads
831
Kommentare
2
Gefällt mir
97
Einbettungen 0
No embeds

Inhalte melden
Als unangemessen gemeldet Als unangemessen melden
Als unangemessen melden

Wählen Sie Ihren Grund, warum Sie diese Präsentation als unangemessen melden.

Löschen
No notes for slide
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • Shows relationships – real or accidental; “the gestalt”\n
  • Ordering is critical, shows strength of relationships\n
  • Order critical, shows relationships between groups; it can all be made to “fit”\n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • \n
  • Transcript

    • 1. Visualizing Networks Beyond the “Hairball” Lynn Cherny @arnicas O’Reilly Strata NYC 2012
    • 2. Visualizing Networks Beyond the “Hairball” Lynn Cherny @arnicas O’Reilly Strata NYC 2012
    • 3. PS(A): I AM NOT JASONSUNDRAMHe could not make it, and asked me to take over. 2
    • 4. The Hairball: A Metaphor for Complexityhttp://www.nd.edu/~networks/Publication%20Categories/01%20Review%20Articles/ScaleFree_Scientific%20Ameri%20288,%2060-69%20(2003).pdf
    • 5. http://www.nd.edu/~networks/Publication%20Categories/01%20Review%20Articles/ScaleFree_Scientific%20Ameri%20288,%2060-69%20(2003).pdf
    • 6. http://www.linkedin.com/today/post/article/20121016185655-10842349-the-hidden-power-
    • 7. WHAT IS A NETWORK?It’s not a visualization. Think of it as a data structure.
    • 8. Data relationship: entities + relationshipsto other objects (node/edge, vertex/link)Nodes and Edges may have attributes, eg. gender, age, weight, tv prefs connection date, frequency of contact, type of exchange, directionality of relationship attributes may be calculated from network itself
    • 9. Lane Harrison: http://blog.visual.ly/network-visualizations/
    • 10. Lane Harrison: http://blog.visual.ly/network-visualizations/
    • 11. Lane Harrison: http://blog.visual.ly/network-visualizations/
    • 12. Best! A User Study on Visualizing Directed Edges in Graphs” Danny Holten and Jarke J. van Wijk, 27th SIGCHI Conference on Human Factors in Computing Systems (Proceedings of CHI 2009),http://blog.visual.ly/network-visualizations/ 9
    • 13. 10
    • 14. 10
    • 15. 10
    • 16. It’s a natural human trait to see visual similarity and proximity asmeaningful.Be very careful about your display choices and layout methods! 10
    • 17. Reading a network visualization There’s obviously something important going on here, structurally....
    • 18. Reading a network visualization Lo o k a t th is o utl ier cas e! There’s obviously something important going on here, structurally....
    • 19. Reading a network visualization à Lo o age k a mé n ere t th A ver h is o i s o utl tro ier cas e! There’s obviously something important going on here, structurally....
    • 20. S? MIReading a network visualization à Lo o age k a mé n ere t th A ver h is o i s o utl tro ier cas e! There’s obviously something important going on here, structurally....Using a “random” Gephi layout on the dolphins
    • 21. S? MIReading a network visualization à Lo o age k a mé n ere t th A ver h is o i s o utl tro ier cas e! Rando m! There’s obviously something important going on here, structurally....Using a “random” Gephi layout on the dolphins
    • 22. Design Examples s! lp h in o it h D w
    • 23. The Dolphins of Doubtful Soundhttp://www.doc.govt.nz/documents/conservation/native-animals/marine-mammals/abundance-population-structure-bottlenose-dolphins-doubtful-dusky-sounds.pdf
    • 24. “The bottlenose dolphin community of DoubtfulSound features a large proportion of long-lastingassociations. Can geographic isolation explain thisunique trait?”David Lusseau et al. BEHAVIORAL ECOLOGY ANDSOCIOBIOLOGY Volume 54, Number 4 (2003)http://www.springerlink.com/content/pepxvj4lu42ur2gw/
    • 25. “The bottlenose dolphin community of DoubtfulSound features a large proportion of long-lastingassociations. Can geographic isolation explain thisunique trait?” ! ti tle p er l pa tua ac e ThDavid Lusseau et al. BEHAVIORAL ECOLOGY ANDSOCIOBIOLOGY Volume 54, Number 4 (2003)http://www.springerlink.com/content/pepxvj4lu42ur2gw/
    • 26. “SF” “ULT” 2 hung outD. Lusseau, Evidence for Social Role in a Dolphin Social Network.Evol Ecol (2007) 21:357–366
    • 27. Citation: Lusseau D (2007) Why Are Male Social Relationships Complex in the Doubtful Sound Bottlenose Dolphin Population?. PLoS ONE 2(4):e348. doi:10.1371/journal.pone.0000348
    • 28. Citation: Lusseau D (2007) Why Are Male Social Relationships Complex in the Doubtful Sound Bottlenose Dolphin Population?. PLoS ONE 2(4):e348. doi:10.1371/journal.pone.0000348 define relationship Using “mirroring” to
    • 29. Citation: Lusseau D (2007) Why Are Male Social Relationships Complex in the Doubtful Sound Bottlenose Dolphin Population?. PLoS ONE 2(4):e348. doi:10.1371/journal.pone.0000348 define edges “headbutting” to define relationship UsingUsing “mirroring” to
    • 30. TOOLS FOR TODAYCreating network layouts... 17
    • 31. Gephi 18
    • 32. Or “D3” (d3js.org)• A “build it yourself” svg-based visualization library• Import graphs as (or parse to create) a json node-link structure
    • 33. Making a NetworkWho is your audience? What’s the goal? Exploration / Iterative visualization during data analysis? End-user communication?
    • 34. Making a NetworkWho is your audience? What’s the goal? Exploration / Iterative visualization during data analysis? End-user communication? Layout choices: by hand, algorithmic, style... Understand the global and local context with some stats about actors and roles in the network Improve your layout with stats / attributes - inherent (such as gender) or calculated (e.g., degree) Add interactivity for end users if appropriate
    • 35. J Bertin: Semiologyof Graphics Linear Circular Irregular Regular (Tree) 3D Matrix / BipartiteBertin, J. Semiology of Graphics: Diagrams,Networks, Maps (1967)
    • 36. Algorithmic ApproachesFrank van Ham talk slides: http://bit.ly/s6udpy
    • 37. (Trees are a whole other subject)Treevis.net: http://www.informatik.uni-rostock.de/~hs162/treeposter/poster.html
    • 38. MATRIX LAYOUTS /REPRESENTATIONS
    • 39. http://barabasilab.neu.edu/networksciencebook/download/network_science_October_2012.pdf
    • 40. Real social networks are generally quite sparse.http://www.cise.ufl.edu/research/sparse/matrices/Newman/dolphins.html
    • 41. D3 demo by me http://www.ghostweather.com/essays/talks/networkx/adjacency.html
    • 42. NodeTrix: A Hybrid Visualization of Social Networks. Nathalie Henry, Jean-Daniel Fekete, andMichael J. McGuffin. (2007) http://arxiv.org/abs/0705.0599
    • 43. http://semilattice.net/eyeodiff/
    • 44. http://semilattice.net/eyeodiff/
    • 45. ARC / LINEAR LAYOUTS
    • 46. Philipp Steinweber and Andreas Koller Similar Diversity, 2007For a D3 example in another domain: http://tradearc.laserdeathstehr.com/
    • 47. http://www.openbible.info/blog/2010/04/bible-cross-references-visualization/
    • 48. Hive PlotsD3: http://bost.ocks.org/mike/hive/
    • 49. http://mariandoerk.de/pivotpaths/
    • 50. Design Interlude
    • 51. Bertin’s Thought ProcessBertin, J. Semiology of Graphics: Diagrams, Networks, Maps (1967)
    • 52. (P) Paris(Z) Paris Suburbs(+50) Communes of >50K(+10) Communes of >10K(-10) Communes of <10K(R) Rural
    • 53. CIRCULAR / CHORD LAYOUTS
    • 54. “If its Circos pro bab rou ly d nd, o it Circ os ” canhttp://circos.ca/images/
    • 55. Simple Orderings of Nodes Circular Layout “Dual Circle” layout with Sorted by ordered by Degree most popular dolphin in center Modularity 41Dolphins colored by modularity class (community) in Gephi
    • 56. http://www.ghostweather.com/essays/talks/networkx/chord.html
    • 57. http://www.ghostweather.com/essays/talks/networkx/chord.html
    • 58. Hierarchical Edge Bundling"Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data”, Danny Holten, IEEETransactions on Visualization and Computer Graphics (TVCG; Proceedings of Vis/InfoVis 2006), Vol. 12, No. 5,
    • 59. A D3 Example by M. BostockD3: http://bl.ocks.org/1044242
    • 60. A very short detour into maps...
    • 61. 46Flow Map Layout, Phan et al (2005) http://graphics.stanford.edu/papers/flow_map_layout/
    • 62. 46Flow Map Layout, Phan et al (2005) http://graphics.stanford.edu/papers/flow_map_layout/
    • 63. "Force-Directed Edge Bundling for Graph Visualization”,Danny Holten and Jarke J. van Wijk, 11th Eurographics/IEEE-VGTC Symposium on Visualization (ComputerGraphics Forum; Proceedings of EuroVis 2009), Pages 983 - 990, 2009.
    • 64. Divided Edge Bundling for Directional Network DataDavid Selassie, Brandon Heller, Jeffrey HeerIEEE Trans. Visualization & Comp. Graphics (Proc. InfoVis), 2011 48
    • 65. Design Example
    • 66. 50http://jeromecukier.net/projects/agot/places.html
    • 67. 50http://jeromecukier.net/projects/agot/places.html
    • 68. 51Jerome’s version with the map is not available online, sorry.
    • 69. Yet Another Design Example
    • 70. Moritz Stefaner’s Muesli Problemhttps://speakerdeck.com/u/moritzstefaner/p/omg-its-all-connected
    • 71. https://speakerdeck.com/u/moritzstefaner/p/omg-its-all-connected
    • 72. s li ke iou ch lic u m ot de o “ To s, n h!” el f o om oug ims hr en to h us m i tz or -Mhttps://speakerdeck.com/u/moritzstefaner/p/omg-its-all-connected
    • 73. Finalhttps://speakerdeck.com/u/moritzstefaner/p/omg-its-all-connected
    • 74. ALGORITHMIC LAYOUTSGephi / D3.js / Other tools
    • 75. Gephi  Sigma.jsGephi.org: Open source, runs on Mac, Linux, PC Can be run from a python-esque console plugin or UI Can be run “headless” for layouts (Jason Sundram) https://github.com/jsundram/pygephi Plugins include a Neo4j graph db access, and streaming supportSigma.js : Will display a gexf gephi layout file with minimal work, using a plugin interpreter for sigma Also offers a force-directed layout plugin for graphs without x&y coords Does CANVAS drawing, not SVG
    • 76. http://www.barabasilab.com/pubs/CCNR-ALB_Publications/200705-14_PNAS-HumanDisease/Suppl/
    • 77. Movie:Sigma.js version of the Gephi export http://exploringdata.github.com/vis/human-disease-
    • 78. Using Sigma.js basic_sigma.js<div class="sigma-expand“ function init() { // Instanciate sigma.js and customize rendering : id="sigma-example"></div> var sigInst = sigma.init(document.getElementById(sigma-example)) .drawingProperties({ defaultLabelColor: #fff, defaultLabelSize: 14, defaultLabelBGColor: #fff, defaultLabelHoverColor: #000, labelThreshold: 6, defaultEdgeType: curve }).graphProperties({ minNodeSize: 0.5, maxNodeSize: 5, minEdgeSize: 1, maxEdgeSize: 1 }).mouseProperties({ Where maxRatio: 32 }); to put // Parse a GEXF encoded file to fill the graph the<script src="../js/sigma.min.js"></script> // (requires "sigma.parseGexf.js" to be included)<script src="../js/sigma.parseGexf.js"></script> sigInst.parseGexf(color_by_mod.gexf); graph<script src="basic_sigma.js"></script> // Draw the graph : sigInst.draw(); } Your graph if (document.addEventListener) { document.addEventListener("DOMContentLoaded", init, false); } else { window.onload = init; } In sigma.js’s github (under plugins!)
    • 79. Sample Layout Plugins in Gephihttps://gephi.org/tutorials/gephi-tutorial-layouts.pdf
    • 80. Gephi Plugin Layout Details Layout Complexity Graph Size Author Comment Circular O(N) 1 to 1M nodes Matt Groeninger Used to show distribution, ordered layout Radial Axis O(N) 1 to 1M nodes Matt Groeninger Show ordered groups (homophily) Force Atlas O(N²) 1 to 10K nodes Mathieu Jacomy Slow, but uses edge weight and few biases Force Atlas 2 O(N*log(N)) 1 to 1M nodes Mathieu (does not use Jacomy weight) OpenOrd O(N*log(N)) 100 to 1M nodes S. Martin, W. M. Focus on clustering Brown, R. Klavans, (uses edge weight) Yifan Hu O(N*log(N)) 100 to 100K nodes and K. Boyack Yifan Hu (no edge weight) Fruchterman- O(N²) 1 to 1K nodes Fruchterman & Particle system, slow Rheingold Rheingold! (no edge weight) GeoLayout O(N) 1 to 1M nodes Alexis Jacomy Uses Lat/Long for layouthttps://gephi.org/2011/new-tutorial-layouts-in-gephi/
    • 81. Dolphins AgainOpenOrd + “No Overlap” ForceAtlas2 63
    • 82. Dolphins AgainOpenOrd + “No Overlap” ForceAtlas2 63
    • 83. Dolphins AgainOpenOrd + “No Overlap” ForceAtlas2 63
    • 84. Unweighted dolphins, Force AtlasWeight 2: Force Atlas Weight 4: Force Atlas 64
    • 85. Unweighted dolphins, Force AtlasWeight 2: Force Atlas Weight 4: Force Atlas Weight 4: Yifan Hu 64
    • 86. Canvas/SVG benchmarks from the d3.js group: https://docs.google.com/spreadsheet/ ccc?Nick Diakapolous: http://nad.webfactional.com/ntap/graphscale/ key=0AtvlFoSBUC5kdEZJNVFySG9wSHZk
    • 87. Canvas/SVG benchmarks from the d3.js group: https://docs.google.com/spreadsheet/ ccc?Nick Diakapolous: http://nad.webfactional.com/ntap/graphscale/ key=0AtvlFoSBUC5kdEZJNVFySG9wSHZk
    • 88. SIMPLE CALCULATIONS ONNETWORKS CAN TELL YOUOften you need to visualize the structure/role of the graphelements as part of the visualization: So, do some simplemath.
    • 89. Degree (In, Out) “Degree” is a measure of the edges in (directed), out (directed), or total (in directed or undirected graphs) to a node “Hub” nodes have high in- degree. In scale-free networks, we see preferential attachment to the popular kids.http://mlg.ucd.ie/files/summer/tutorial.pdf
    • 90. Scale-free NetworksImage from Lada Adamic’s SNA Course on Coursera pdf 3D
    • 91. The Threat of Hub-LossAlbert-László Barabási and Eric Bonabeau, Scale-Free Networks, 2003.http://www.scientificamerican.com/article.cfm?id=scale-free-networks
    • 92. Visualization Aside: If Some Names are Huge, the Others are Invisible-? 70Correcting for text size by degree display issue
    • 93. Visualization Aside: If Some Names are Huge, the Others are Invisible-? Gephi Panel 70Correcting for text size by degree display issue
    • 94. Visualization Aside: If Some Names are Huge, the Others are Invisible-? Gephi Panel 70Correcting for text size by degree display issue
    • 95. Visualization Aside: If Some Names are Huge, the Others are Invisible-? Gephi Panel 70Correcting for text size by degree display issue
    • 96. Betweenness A measure of connectedness between (sub)components of the graph “Betweenness centrality thus tends to pick out boundary individuals who play the role of brokers between communities.”Lusseau and Newman. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1810112/pdf/15801609.pdf http://en.wikipedia.org/wiki/Centrality#Betweenness_centrality
    • 97. Judging By Eye Will Probably BeWrong... 72
    • 98. Judging By Eye Will Probably BeWrong... ? This one? 72
    • 99. Judging By Eye Will Probably BeWrong... ? This one? Sized by Betweenness 72
    • 100. Eigenvector Centrality Intuition: A node is important if it is connected to other important nodes A node with a small number of influential contacts may outrank one with a larger number of mediocre contactshttp://mlg.ucd.ie/files/summer/tutorial.pdf http://demonstrations.wolfram.com/
    • 101. PagerankWikipedia image
    • 102. Community Detection E.g., the Louvain method, in Gephi as “Modularity.” Many layout algorithms help you intuit these structures, but don’t rely on perception of layout!http://en.wikipedia.org/wiki/File:Network_Community_Structure.png
    • 103. Citation: Lusseau D (2007) Why Are Male Social Relationships Complex in the Doubtful SoundBottlenose Dolphin Population?. PLoS ONE 2(4): e348. doi:10.1371/journal.pone.0000348
    • 104. Citation: Lusseau D (2007) Why Are Male Social Relationships Complex in the Doubtful SoundBottlenose Dolphin Population?. PLoS ONE 2(4): e348. doi:10.1371/journal.pone.0000348 77
    • 105. Identifying the role that animals play in their social networks (2004)D Lusseau, MEJ NewmanProceedings of the Royal Society of London. Series B: Biological Sciences
    • 106. Identifying the role that animals play in their social networks (2004)D Lusseau, MEJ NewmanProceedings of the Royal Society of London. Series B: Biological Sciences
    • 107. Identifying the role that animals play in their social networks (2004)D Lusseau, MEJ NewmanProceedings of the Royal Society of London. Series B: Biological Sciences
    • 108. Identifying the role that animals play in their social networks (2004)D Lusseau, MEJ NewmanProceedings of the Royal Society of London. Series B: Biological Sciences
    • 109. Identifying the role that animals play in their social networks (2004)D Lusseau, MEJ NewmanProceedings of the Royal Society of London. Series B: Biological Sciences
    • 110. Identifying the role that animals play in their social networks (2004)D Lusseau, MEJ NewmanProceedings of the Royal Society of London. Series B: Biological Sciences
    • 111. Eduarda Mendes Rodrigues, Natasa Milic-Frayling, Marc Smith, Ben Shneiderman, Derek Hansen, Group-in-a-box Layout for Multi-faceted Analysis of Communities. IEEE Third International Conference on Social
    • 112. http://mbostock.github.com/d3/talk/20111116/force-collapsible.html
    • 113. http://mbostock.github.com/d3/talk/20111116/force-collapsible.html
    • 114. Movie:Ger Hobbelt D3: http://bl.ocks.org/3616279
    • 115. Movie:Ger Hobbelt D3: http://bl.ocks.org/3616279
    • 116. Design Example
    • 117. 100 nodes, size by degree, Clustered by partition, no edges shaded by Betweenness, in until you click on one, node size is a d3.js force directed layout. choice of attributes, nodes represented by labels/colors….http://www.ghostweather.com/essays/talks/networkx/ http://www.ghostweather.com/essays/talks/networkx/
    • 118. Movie: 84http://www.ghostweather.com/essays/talks/networkx/force_fonts.html
    • 119. Movie: by @moebio 85http://intuitionanalytics.com/pleiades/
    • 120. ! LOLSO YOU WANT TO LAY IT OUTYOURSELF...Perfectionist? Artist? Don’t like algorithms? 86
    • 121. Jeff Heer: http://hci.stanford.edu/courses/cs448b/f11/lectures/CS448B-20111110-
    • 122. Design Examples
    • 123. Conspiracy Theorist Mark Learning from Lombardi: http://benfry.com/exd09/
    • 124. Conspiracy Theorist Mark Learning from Lombardi: http://benfry.com/exd09/
    • 125. Stefanie Posavechttp://www.itsbeenreal.co.uk/index.php?/wwwords/literary-
    • 126. Roche Applied Science Biochemical Pathways Map: http://web.expasy.org/cgi-bin/pathways/
    • 127. Roche Applied Science Biochemical Pathways Map: http://web.expasy.org/cgi-bin/pathways/
    • 128. Hybrid Method: Use algorithmic layout, and then adjust nodes byGer Hobbelt in D3: http://bl.ocks.org/3637711
    • 129. Tweaking your layout is addictive! Yo ned wa u r ha ! ve ! be enJason Davies: http://www.jasondavies.com/planarity/
    • 130. STEP BACK, SCALE UP... 94
    • 131. C.Dunne & B.Shneiderman. Network Motif Simplification. http://hcil2.cs.umd.edu/trs/
    • 132. GraphPrism: Compact Visualization of Network StructureSanjay Kairam, Diana MacLean, Manolis Savva, Jeffrey HeerAdvanced Visual Interfaces, 2012
    • 133. 97http://openaccess.city.ac.uk/1324/
    • 134. Video: 98http://graphics.wsj.com/political-moneyball/
    • 135. Wrap it up on design...
    • 136. RemindersChoose your visual encodings, layout,interaction to make it a visualization,rather than raw data vomit. Take care: people will infer things from proximity/similarity even if it was not intended!Do data analysis / reduction - whywould you want to show 1T of networkdata?Allow interactivity if needed for endusers. Help people find things in your network!
    • 137. RemindersChoose your visual encodings, layout,interaction to make it a visualization,rather than raw data vomit. Take care: people will infer things from To ot sci proximity/similarity even if it was not =N o m wa ce d intended! at uc al en a h d goDo data analysis / reduction - why at ys ! awould you want to show 1T of network o ddata?Allow interactivity if needed for endusers. Help people find things in your network!
    • 138. More Reminders!Different layouts communicate differentthings to your viewer - choose wiselyReducing noise: Don’t show edges (perhaps on demand) Show details only on demand: zoom in Cluster your nodes/edges Consider if it has to be a “network” display at all: Is it the stats you care about? Or the hairball? 101
    • 139. The Map is Not the Territory…Forest Pitts (thanks to Noah Friedkin) http://www.analytictech.com/networks/pitts.htm
    • 140. The Map is Not the Territory…Forest Pitts (thanks to Noah Friedkin) http://www.analytictech.com/networks/pitts.htm
    • 141. www.Visualcomplexity.com
    • 142. Thanks! @arnicas lynn@ghostweather.comAnd thanks to twitter vis friends for content: @jsundram, @laneharrison, @moritz_stefaner, @jcukier,@jeff1024, @vlandham, @moebio, @jeffrey_heer, @mbostock, @eagereyes, @jasondavies, @stefpos, @sarahslo, @ndiakopoulos, @gephi
    • 143. A Few More ReferencesJeff Heer class slides: http:// Robert Kosara’s post: http://hci.stanford.edu/courses/cs448b/ eagereyes.org/techniques/graphs-w09/lectures/20090204- hairballGraphsAndTrees.pdf Lane Harrison’s post: http://A great in-progress book on networks: blog.visual.ly/network-http://barabasilab.neu.edu/ visualizations/networksciencebook/ MS Lima’s book Visual ComplexityMark Newman’s many papers: http:// Jason Sundram’s tool to drive Gephiwww-personal.umich.edu/~mejn/ layout from command line: https://Eyeo Festival videos from Moritz github.com/jsundram/pygephiStefaner, Manuel Lima, Stefanie A couple articles on communityPosavec structure:Journal of Graph Algorithms and Overlapping Community DetectionApplications: http://jgaa.info/ in Networks: State of the Arthome.html and Comparative Study by JieruiJim Vallandingham’s D3 network Xie, Stephen Kelley, Boleslaw K.tutorials: http://flowingdata.com/ Szymanski2012/08/02/how-to-make-an- Empirical Comparison ofinteractive-network-visualization/, Algorithms for Network

    ×