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Visualizing Networks: Beyond the Hairball

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

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Visualizing Networks
      Beyond the “Hairball”

           Lynn Cherny
             @arnicas

     O’Reilly Strata NYC 2012
Visualizing Networks
      Beyond the “Hairball”

           Lynn Cherny
             @arnicas

     O’Reilly Strata NYC 2012
PS(A): I AM NOT JASON
SUNDRAM
He could not make it, and asked me to take over.


                                                   2
The Hairball: A Metaphor for
                      Complexity




http://www.nd.edu/~networks/Publication%20Categories/01%20Review%20Articles/ScaleFree_Scientific%20Ameri
%20288,%2060-69%20(2003).pdf
http://www.nd.edu/~networks/Publication%20Categories/01%20Review%20Articles/ScaleFree_Scientific%20Ameri
%20288,%2060-69%20(2003).pdf
http://www.linkedin.com/today/post/article/20121016185655-10842349-the-hidden-power-

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Visualizing Networks: Beyond the Hairball

  • 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 JASON SUNDRAM He could not make it, and asked me to take over. 2
  • 4. The Hairball: A Metaphor for Complexity http://www.nd.edu/~networks/Publication%20Categories/01%20Review%20Articles/ScaleFree_Scientific%20Ameri %20288,%2060-69%20(2003).pdf
  • 7. WHAT IS A NETWORK? It’s not a visualization. Think of it as a data structure.
  • 8. Data relationship: entities + relationships to 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
  • 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 as meaningful. 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 Sound http://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 Doubtful Sound features a large proportion of long-lasting associations. Can geographic isolation explain this unique trait?” David Lusseau et al. BEHAVIORAL ECOLOGY AND SOCIOBIOLOGY Volume 54, Number 4 (2003) http://www.springerlink.com/content/pepxvj4lu42ur2gw/
  • 25. “The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations. Can geographic isolation explain this unique trait?” ! ti tle p er l pa tua ac e Th David Lusseau et al. BEHAVIORAL ECOLOGY AND SOCIOBIOLOGY Volume 54, Number 4 (2003) http://www.springerlink.com/content/pepxvj4lu42ur2gw/
  • 26. “SF” “ULT” 2 hung out D. 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 TODAY Creating 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 Network Who is your audience? What’s the goal? Exploration / Iterative visualization during data analysis? End-user communication?
  • 34. Making a Network Who 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: Semiology of Graphics Linear Circular Irregular Regular (Tree) 3D Matrix / Bipartite Bertin, J. Semiology of Graphics: Diagrams, Networks, Maps (1967)
  • 36. Algorithmic Approaches Frank 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
  • 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, and Michael J. McGuffin. (2007) http://arxiv.org/abs/0705.0599
  • 45. ARC / LINEAR LAYOUTS
  • 46. Philipp Steinweber and Andreas Koller Similar Diversity, 2007 For a D3 example in another domain: http://tradearc.laserdeathstehr.com/
  • 51. Bertin’s Thought Process Bertin, J. Semiology of Graphics: Diagrams, Networks, Maps (1967)
  • 53. (P) Paris (Z) Paris Suburbs (+50) Communes of >50K (+10) Communes of >10K (-10) Communes of <10K (R) Rural
  • 54. CIRCULAR / CHORD LAYOUTS
  • 55. “If it's Circos pro bab rou ly d nd, o it Circ os ” can http://circos.ca/images/
  • 56. Simple Orderings of Nodes Circular Layout “Dual Circle” layout with Sorted by ordered by Degree most popular dolphin in center Modularity 41 Dolphins colored by modularity class (community) in Gephi
  • 59. Hierarchical Edge Bundling "Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data”, Danny Holten, IEEE Transactions on Visualization and Computer Graphics (TVCG; Proceedings of Vis/InfoVis 2006), Vol. 12, No. 5,
  • 60. A D3 Example by M. Bostock D3: http://bl.ocks.org/1044242
  • 61. A very short detour into maps...
  • 62. 46 Flow Map Layout, Phan et al (2005) http://graphics.stanford.edu/papers/flow_map_layout/
  • 63. 46 Flow Map Layout, Phan et al (2005) http://graphics.stanford.edu/papers/flow_map_layout/
  • 64. "Force-Directed Edge Bundling for Graph Visualization”, Danny Holten and Jarke J. van Wijk, 11th Eurographics/IEEE-VGTC Symposium on Visualization (Computer Graphics Forum; Proceedings of EuroVis 2009), Pages 983 - 990, 2009.
  • 65. Divided Edge Bundling for Directional Network Data David Selassie, Brandon Heller, Jeffrey Heer IEEE Trans. Visualization & Comp. Graphics (Proc. InfoVis), 2011 48
  • 69. 51 Jerome’s version with the map is not available online, sorry.
  • 71. Moritz Stefaner’s Muesli Problem https://speakerdeck.com/u/moritzstefaner/p/omg-its-all-connected
  • 73. 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 -M https://speakerdeck.com/u/moritzstefaner/p/omg-its-all-connected
  • 75. ALGORITHMIC LAYOUTS Gephi / D3.js / Other tools
  • 76. Gephi  Sigma.js Gephi.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 support Sigma.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
  • 78. Movie: Sigma.js version of the Gephi export http://exploringdata.github.com/vis/human-disease-
  • 79. 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!)
  • 80. Sample Layout Plugins in Gephi https://gephi.org/tutorials/gephi-tutorial-layouts.pdf
  • 81. 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 layout https://gephi.org/2011/new-tutorial-layouts-in-gephi/
  • 82. Dolphins Again OpenOrd + “No Overlap” ForceAtlas2 63
  • 83. Dolphins Again OpenOrd + “No Overlap” ForceAtlas2 63
  • 84. Dolphins Again OpenOrd + “No Overlap” ForceAtlas2 63
  • 85. Unweighted dolphins, Force Atlas Weight 2: Force Atlas Weight 4: Force Atlas 64
  • 86. Unweighted dolphins, Force Atlas Weight 2: Force Atlas Weight 4: Force Atlas Weight 4: Yifan Hu 64
  • 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. Canvas/SVG benchmarks from the d3.js group: https://docs.google.com/spreadsheet/ ccc? Nick Diakapolous: http://nad.webfactional.com/ntap/graphscale/ key=0AtvlFoSBUC5kdEZJNVFySG9wSHZk
  • 89. SIMPLE CALCULATIONS ON NETWORKS CAN TELL YOU Often you need to visualize the structure/role of the graph elements as part of the visualization: So, do some simple math.
  • 90. 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
  • 91. Scale-free Networks Image from Lada Adamic’s SNA Course on Coursera pdf 3D
  • 92. The Threat of Hub-Loss Albert-László Barabási and Eric Bonabeau, Scale-Free Networks, 2003.http://www.scientificamerican.com/ article.cfm?id=scale-free-networks
  • 93. Visualization Aside: If Some Names are Huge, the Others are Invisible-? 70 Correcting for text size by degree display issue
  • 94. Visualization Aside: If Some Names are Huge, the Others are Invisible-? Gephi Panel 70 Correcting for text size by degree display issue
  • 95. Visualization Aside: If Some Names are Huge, the Others are Invisible-? Gephi Panel 70 Correcting for text size by degree display issue
  • 96. Visualization Aside: If Some Names are Huge, the Others are Invisible-? Gephi Panel 70 Correcting for text size by degree display issue
  • 97. 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
  • 98. Judging By Eye Will Probably Be Wrong... 72
  • 99. Judging By Eye Will Probably Be Wrong... ? This one? 72
  • 100. Judging By Eye Will Probably Be Wrong... ? This one? Sized by Betweenness 72
  • 101. 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 contacts http://mlg.ucd.ie/files/summer/tutorial.pdf http://demonstrations.wolfram.com/
  • 103. 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
  • 104. 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
  • 105. 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 77
  • 106. Identifying the role that animals play in their social networks (2004) D Lusseau, MEJ Newman Proceedings 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 Newman Proceedings 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 Newman Proceedings 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 Newman Proceedings 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 Newman Proceedings of the Royal Society of London. Series B: Biological Sciences
  • 111. Identifying the role that animals play in their social networks (2004) D Lusseau, MEJ Newman Proceedings of the Royal Society of London. Series B: Biological Sciences
  • 112. 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
  • 115. Movie: Ger Hobbelt D3: http://bl.ocks.org/3616279
  • 116. Movie: Ger Hobbelt D3: http://bl.ocks.org/3616279
  • 118. 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/
  • 119. Movie: 84 http://www.ghostweather.com/essays/talks/networkx/force_fonts.html
  • 120. Movie: by @moebio 85 http://intuitionanalytics.com/pleiades/
  • 121. ! LOL SO YOU WANT TO LAY IT OUT YOURSELF... Perfectionist? Artist? Don’t like algorithms? 86
  • 124. Conspiracy Theorist Mark Learning from Lombardi: http://benfry.com/exd09/
  • 125. Conspiracy Theorist Mark Learning from Lombardi: http://benfry.com/exd09/
  • 127. Roche Applied Science Biochemical Pathways Map: http://web.expasy.org/cgi-bin/pathways/
  • 128. Roche Applied Science Biochemical Pathways Map: http://web.expasy.org/cgi-bin/pathways/
  • 129. Hybrid Method: Use algorithmic layout, and then adjust nodes by Ger Hobbelt in D3: http://bl.ocks.org/3637711
  • 130. Tweaking your layout is addictive! Yo ned wa u r ha ! ve ! be en Jason Davies: http://www.jasondavies.com/planarity/
  • 131. STEP BACK, SCALE UP... 94
  • 132. C.Dunne & B.Shneiderman. Network Motif Simplification. http://hcil2.cs.umd.edu/trs/
  • 133. GraphPrism: Compact Visualization of Network Structure Sanjay Kairam, Diana MacLean, Manolis Savva, Jeffrey Heer Advanced Visual Interfaces, 2012
  • 135. Video: 98 http://graphics.wsj.com/political-moneyball/
  • 136. Wrap it up on design...
  • 137. Reminders Choose 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 - why would you want to show 1T of network data? Allow interactivity if needed for end users. Help people find things in your network!
  • 138. Reminders Choose 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 go Do data analysis / reduction - why at ys ! a would you want to show 1T of network o d data? Allow interactivity if needed for end users. Help people find things in your network!
  • 139. More Reminders! Different layouts communicate different things to your viewer - choose wisely Reducing 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
  • 140. The Map is Not the Territory… Forest Pitts (thanks to Noah Friedkin) http://www.analytictech.com/networks/pitts.htm
  • 141. The Map is Not the Territory… Forest Pitts (thanks to Noah Friedkin) http://www.analytictech.com/networks/pitts.htm
  • 143. Thanks! @arnicas lynn@ghostweather.com And thanks to twitter vis friends for content: @jsundram, @laneharrison, @moritz_stefaner, @jcukier, @jeff1024, @vlandham, @moebio, @jeffrey_heer, @mbostock, @eagereyes, @jasondavies, @stefpos, @sarahslo, @ndiakopoulos, @gephi
  • 144. A Few More References Jeff Heer class slides: http:// Robert Kosara’s post: http:// hci.stanford.edu/courses/cs448b/ eagereyes.org/techniques/graphs- w09/lectures/20090204- hairball GraphsAndTrees.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 Complexity Mark Newman’s many papers: http:// Jason Sundram’s tool to drive Gephi www-personal.umich.edu/~mejn/ layout from command line: https:// Eyeo Festival videos from Moritz github.com/jsundram/pygephi Stefaner, Manuel Lima, Stefanie A couple articles on community Posavec structure: Journal of Graph Algorithms and Overlapping Community Detection Applications: http://jgaa.info/ in Networks: State of the Art home.html and Comparative Study by Jierui Jim Vallandingham’s D3 network Xie, Stephen Kelley, Boleslaw K. tutorials: http://flowingdata.com/ Szymanski 2012/08/02/how-to-make-an- Empirical Comparison of interactive-network-visualization/, Algorithms for Network

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