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Visualizing Networks

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This document discusses various techniques for visualizing networks, including different layout algorithms. It begins by defining what a network is as a data structure of entities and relationships. It then covers topics like matrix representations, arc/linear layouts, circular/chord layouts, and hierarchical edge bundling. It also discusses simple network measures like degree and betweenness centrality that can provide insight into a network's structure. The document provides many examples and references to external resources on network visualization.

Data & Analytics
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Visualizing Networks Lynn Cherny @arnicas 4 Nov 2014
The  Hairball:  A  Metaphor  for   Complexity h6p://www.nd.edu/~networks/PublicaBon%20Categories/01%20Review%20ArBcles/ScaleFree_ScienBfic%20Ameri%20288,%2060-­‐69%20(2003).pdf
h6p://www.linkedin.com/today/post/arBcle/20121016185655-­‐10842349-­‐the-­‐hidden-­‐power-­‐of-­‐networks
Release by Joint Economic Committee minority Republicans led by Boehner (2010), Iliinsky & Steele fig 4-14 4
Release by Joint Economic Committee minority Republicans led by Boehner (2010), Iliinsky & Steele fig 4-14 Neither Exploratory Nor Explanatory: Political. 4
Redesign by Robert Palmer (Iliisnky & Steele fig 4-15) 5
Redesign by Robert Palmer (Iliisnky & Steele fig 4-15) “By releasing your chart, instead of meaningfully educating the public, you willfully obfuscated an already complicated proposal.There is no simple proposal to solve this problem.You instead chose to shout ‘12! 16! 37! 9! 24!’ while we were trying to count something.” 5
WHAT  IS  A  NETWORK? It’s  not  a  visualizaBon.    Think  of  it  as  a  data  structure.
Data  relaBonship:  enBBes  +  relaBonships  to  other   objects  (node/edge,  vertex/link)   Nodes  and  Edges  may  have  a6ributes,  eg.   gender,  age,  weight,  tv  prefs   connecBon  date,  frequency  of  contact,  type  of  exchange,   direcBonality  of  relaBonship   a6ributes  may  be  calculated  from  network  relaBons  too
Lane  Harrison:  h6p://blog.visual.ly/network-­‐visualizaBons/
Lane  Harrison:  h6p://blog.visual.ly/network-­‐visualizaBons/
Lane  Harrison:  h6p://blog.visual.ly/network-­‐visualizaBons/
9h6p://blog.visual.ly/network-­‐visualizaBons/ 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),
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10 It’s  a  natural  human  trait  to  see  visual  similarity  and  proximity  as  meaningful.   Be  very  careful  about  your  display  choices  and  layout  methods!  
Reading  a  network  visualizaBon There’s obviously something important going on here, structurally....
Reading  a  network  visualizaBon Look at this outlier case! There’s obviously something important going on here, structurally....
Reading  a  network  visualizaBon Look at this outlier case! There’s obviously something important going on here, structurally.... A ménage à trois over here
Reading  a  network  visualizaBon Look at this outlier case! There’s obviously something important going on here, structurally.... A ménage à trois over here MIS? Using  a  “random”  Gephi  layout  on  the  dolphins
Reading  a  network  visualizaBon Look at this outlier case! There’s obviously something important going on here, structurally.... A ménage à trois over here MIS? Using  a  “random”  Gephi  layout  on  the  dolphins Random!
12 TOOLS  AND  CONCEPTS  FOR  TODAY CreaBng  network  layouts...
13 Gephi  (and  a  li6le  D3)
J  BerBn:  Semiology  
 of  Graphics BerBn,  J.  Semiology  of  Graphics:  Diagrams,     Networks,  Maps  (1967) Linear   Circular   Irregular   Regular  (Tree)   3D   Matrix  /   BiparBte
J  BerBn:  Semiology  
 of  Graphics BerBn,  J.  Semiology  of  Graphics:  Diagrams,     Networks,  Maps  (1967) Linear   Circular   Irregular   Regular  (Tree)   3D   Matrix  /   BiparBte
Algorithmic  Approaches Frank  van  Ham  talk  slides:  h6p://bit.ly/s6udpy
MATRIX  LAYOUTS  /  REPRESENTATIONS
h6p://barabasilab.neu.edu/networksciencebook/download/network_science_October_2012.pdf
Real  social  networks  are   generally  quite  sparse. h6p://www.cise.ufl.edu/research/sparse/matrices/Newman/dolphins.html
D3  demo  by  me  h6p://www.ghostweather.com/essays/talks/networkx/adjacency.html
D3  demo  by  me  h6p://www.ghostweather.com/essays/talks/networkx/adjacency.html
20 Readability:  Matrix  Be6er  (except  for   path  finding) Jean-Daniel Fekete http://lamut.informatik.uni-wuerzburg.de/gd2014/data/uploads/gd2014-fekete-keynote.pdf
ARC  /  LINEAR  LAYOUTS
Philipp  Steinweber  and  Andreas  Koller   Similar  Diversity,  2007 For  a  D3  example  in  another  domain:  h6p://tradearc.laserdeathstehr.com/
Topics  in  Da  Vinci  Code  (by  me) 23 https://www.dropbox.com/s/090vwj12kh3mu96/Screenshot %202014-11-04%2020.45.02.png?dl=0 Lynn Cherny - http://www.ghostweather.com/essays/talks/openvisconf/topic_arc_diagram/TopicArc.html
Topics  in  Da  Vinci  Code  (by  me) 23 https://www.dropbox.com/s/090vwj12kh3mu96/Screenshot %202014-11-04%2020.45.02.png?dl=0 Lynn Cherny - http://www.ghostweather.com/essays/talks/openvisconf/topic_arc_diagram/TopicArc.html
Hive  Plots D3:  h6p://bost.ocks.org/mike/hive/
CIRCULAR  /  CHORD  LAYOUTS
h6p://circos.ca/images/ Circos “If it's round, Circos can probably do it”
h6p://circos.ca/images/ Circos “If it's round, Circos can probably do it” TIL (shocking): It’s all perl code?!
h6p://www.ghostweather.com/essays/talks/networkx/chord.html
h6p://www.ghostweather.com/essays/talks/networkx/chord.html
A  Nice  UI  Improvement  -­‐  DifferenBate  Source/ DesBnaBon 28 Nikola Sander, http://www.global-migration.info/
Hierarchical  Edge  Bundling  demo  by   @mbostock D3:  h6p://bl.ocks.org/1044242 "Hierarchical  Edge  Bundles:  VisualizaBon  of  Adjacency  RelaBons  in  Hierarchical  Data”,  Danny  Holten,  IEEE  TransacBons  on  VisualizaBon   and  Computer  Graphics  (TVCG;  Proceedings  of  Vis/InfoVis  2006),  Vol.  12,  No.  5,  Pages  741  -­‐  748,  2006.
SIMPLE  CALCULATIONS  ON  NETWORKS   CAN  TELL  YOU  LOADS Oven  you  need  to  visualize  the  structure/role  of  the  graph  elements  as   part  of  the  visualizaBon:  So,  do  some  simple  math.
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   preferenBal  a6achment  to  the   popular  kids. h6p://mlg.ucd.ie/files/summer/tutorial.pdf
The  Threat  of  Hub-­‐Loss Albert-­‐László  Barabási  and  Eric  Bonabeau,  Scale-­‐Free  Networks,  2003.h6p://www.scienBficamerican.com/arBcle.cfm?id=scale-­‐free-­‐ networks
VisualizaBon  Aside:  If  Some  Names  are  Huge,   the  Others  are  Invisible-­‐? 33 CorrecBng  for  text  size  by  degree  display  issue
VisualizaBon  Aside:  If  Some  Names  are  Huge,   the  Others  are  Invisible-­‐? 33 Gephi  Panel CorrecBng  for  text  size  by  degree  display  issue
VisualizaBon  Aside:  If  Some  Names  are  Huge,   the  Others  are  Invisible-­‐? 33 Gephi  Panel CorrecBng  for  text  size  by  degree  display  issue
VisualizaBon  Aside:  If  Some  Names  are  Huge,   the  Others  are  Invisible-­‐? 33 Gephi  Panel CorrecBng  for  text  size  by  degree  display  issue
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   communiBes.” h6p://en.wikipedia.org/wiki/Centrality#Betweenness_centrality Lusseau  and  Newman.  h6p://www.ncbi.nlm.nih.gov/pmc/arBcles/ PMC1810112/pdf/15801609.pdf
35 Judging  By  Eye  Will  Probably  Be  Wrong...
35 ? This one? Judging  By  Eye  Will  Probably  Be  Wrong...
35 ? This one? Sized  by  Betweenness Judging  By  Eye  Will  Probably  Be  Wrong...
Eigenvector  Centrality IntuiBon:  A  node  is  important  if  it  is  connected  to   other  important  nodes   A  node  with  a  small  number  of  influenBal  contacts   may  outrank  one  with  a  larger  number  of  mediocre   contacts h6p://mlg.ucd.ie/files/summer/tutorial.pdf h6p://demonstraBons.wolfram.com/NetworkCentralityUsingEigenvectors/
Pagerank Wikipedia  image
Community  DetecBon  Algorithms E.g.,  the  Louvain  method,  in  Gephi  as  “Modularity.”       Many  layout  algorithms  help  you  intuit  these   structures,  but  don’t  rely  on  percepBon  of  layout! h6p://en.wikipedia.org/wiki/File:Network_Community_Structure.png
You  can  even  do  it  in  your  browser  now… 39 http://bl.ocks.org/john-guerra/ecdde32ab4ad91a1a240
You  can  even  do  it  in  your  browser  now… 39 http://bl.ocks.org/john-guerra/ecdde32ab4ad91a1a240
CitaBon:  Lusseau  D  (2007)  Why  Are  Male  Social  RelaBonships  Complex  in  the  Doub|ul  Sound  Bo6lenose  Dolphin   PopulaBon?.  PLoS  ONE  2(4):  e348.  doi:10.1371/journal.pone.0000348
IdenBfying  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
IdenBfying  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
IdenBfying  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
IdenBfying  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
IdenBfying  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
IdenBfying  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
Eduarda  Mendes  Rodrigues,  Natasa  Milic-­‐Frayling,  Marc  Smith,  Ben  Shneiderman,  Derek  Hansen,  Group-­‐in-­‐a-­‐box  Layout  for  MulB-­‐ faceted  Analysis  of  CommuniBes.  IEEE  Third  InternaBonal  Conference  on  Social  CompuBng,  October  9-­‐11,  2011.  Boston,  MA
h6p://mbostock.github.com/d3/talk/20111116/force-­‐collapsible.html Also  see  Ger  Hobbelt  D3:  h6p://bl.ocks.org/3616279
h6p://mbostock.github.com/d3/talk/20111116/force-­‐collapsible.html Also  see  Ger  Hobbelt  D3:  h6p://bl.ocks.org/3616279
ALGORITHMIC  LAYOUTS   Gephi  /  D3.js  /  Other  tools
Gephi  à  Sigma.js  /  D3 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  with  Java  or  Python  (I  have  code  for   this)   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  site  export   Also  offers  a  force-­‐directed  layout  plugin  for  graphs  without  x&y  coords   Does  CANVAS  drawing,  not  SVG    Export  JSON  from  Gephi  and  load  into  D3  network  layout
h6p://www.barabasilab.com/pubs/CCNR-­‐ALB_PublicaBons/200705-­‐14_PNAS-­‐HumanDisease/Suppl/Goh_etal_poster.pdf
Sigma.js  version  of  the  Gephi  export  h6p://exploringdata.github.com/vis/human-­‐disease-­‐network/ Movie:
Sigma.js  version  of  the  Gephi  export  h6p://exploringdata.github.com/vis/human-­‐disease-­‐network/ Movie:
Sample  Layout  Plugins  in  Gephi h6ps://gephi.org/tutorials/gephi-­‐tutorial-­‐layouts.pdf
Gephi  Plugin  Layout  Details Layout Complexity Graph  Size Author Comment Circular O(N) 1  to  1M  nodes Ma6  Groeninger Used  to  show   distribuBon,  ordered   layout Radial  Axis O(N) 1  to  1M  nodes Ma6  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     Jacomy (does  use  weight) OpenOrd O(N*log(N)) 100  to  1M  nodes S.  MarBn,  W.  M.  Brown,   R.  Klavans,  and  K.  Boyack Focus  on  clustering  (uses   edge  weight) Yifan  Hu   O(N*log(N)) 100  to  100K  nodes Yifan  Hu (no  edge  weight) Fruchterman-­‐Rheingold O(N²) 1  to  1K  nodes Fruchterman  &   Rheingold! ParBcle  system,  slow  (no   edge  weight) GeoLayout O(N) 1  to  1M  nodes Alexis  Jacomy Uses  Lat/Long  for  layout h6ps://gephi.org/2011/new-­‐tutorial-­‐layouts-­‐in-­‐gephi/
Dolphins  Again 50 OpenOrd  +  “No  Overlap” ForceAtlas2
Dolphins  Again 50 OpenOrd  +  “No  Overlap” ForceAtlas2
Dolphins  Again 50 OpenOrd  +  “No  Overlap” ForceAtlas2
51 Weight  2:  Force  Atlas Weight  4:  Force  Atlas Unweighted   dolphins,   Force  Atlas
51 Weight  2:  Force  Atlas Weight  4:  Force  Atlas Weight  4:  Yifan  Hu Unweighted   dolphins,   Force  Atlas
Nick  Diakapolous:  h6p://nad.webfacBonal.com/ntap/graphscale/ h6ps://docs.google.com/spreadsheet/ccc? key=0AtvlFoSBUC5kdEZJNVFySG9wSHZka0NsOT ZDSkt3Nnc#gid=0 Canvas/SVG  benchmarks  from  the  d3.js   group:
Nick  Diakapolous:  h6p://nad.webfacBonal.com/ntap/graphscale/ h6ps://docs.google.com/spreadsheet/ccc? key=0AtvlFoSBUC5kdEZJNVFySG9wSHZka0NsOT ZDSkt3Nnc#gid=0 Canvas/SVG  benchmarks  from  the  d3.js   group:
53 FINAL  DESIGN  THOUGHTS…   BY  HAND?  BY  ALGORITHM-­‐?
Jeff  Heer:  h6p://hci.stanford.edu/courses/cs448b/f11/lectures/CS448B-­‐20111110-­‐GraphsAndTrees.pdf
Conspiracy  Theorist  Mark  Lombardi Learning  from  Lombardi:   h6p://benfry.com/exd09/
Conspiracy  Theorist  Mark  Lombardi Learning  from  Lombardi:   h6p://benfry.com/exd09/
J.  BerBn  Again… BerBn,  J.  Semiology  of  Graphics:  Diagrams,  Networks,  Maps  (1967)
Age Groups and Movement to/from Paris to Rural Spots
(P)  Paris   (Z)  Paris  Suburbs   (+50)  Communes  of  >50K   (+10)  Communes  of  >10K   (-­‐10)  Communes  of  <10K   (R)  Rural
Ger  Hobbelt  in  D3:  h6p://bl.ocks.org/3637711 Hybrid  Method:  Use  algorithmic  layout,  and   then  adjust  nodes  by  hand.
Minimize  Info:  Less  is  More 60 Color by important data value, sized by degree, no edges
61 http://www.ghostweather.com/essays/talks/openvisconf/topic_docs_network/
61 http://www.ghostweather.com/essays/talks/openvisconf/topic_docs_network/
Brian  Keegan’s  “15  Minutes  of  Fame  as  a  B-­‐List   GamerGate  Celebrity” 62 http://www.brianckeegan.com/2014/10/my-15-minutes-of-fame-as-a-b-list-gamergate-celebrity/
Brian  Keegan’s  “15  Minutes  of  Fame  as  a  B-­‐List   GamerGate  Celebrity” 62 http://www.brianckeegan.com/2014/10/my-15-minutes-of-fame-as-a-b-list-gamergate-celebrity/
Brian  Keegan’s  “15  Minutes  of  Fame  as  a  B-­‐List   GamerGate  Celebrity” 62 http://www.brianckeegan.com/2014/10/my-15-minutes-of-fame-as-a-b-list-gamergate-celebrity/ Actually:
Wrap it up on design...
Reminders Do  data  analysis  /  reducBon  -­‐  why  would  you   want  to  show  1T  of  network  data?   Calculate  and  reveal  important  facts  about   node  relaBonships.   Consider  your  visual  encodings:     –visualizaBon,  not  data  vomit.   Make  it  interacBve  -­‐  details  on  demand.   Help  people  find  things  in  your  network!  (Search?)   Show  more  on  hover/click
Reminders Do  data  analysis  /  reducBon  -­‐  why  would  you   want  to  show  1T  of  network  data?   Calculate  and  reveal  important  facts  about   node  relaBonships.   Consider  your  visual  encodings:     –visualizaBon,  not  data  vomit.   Make  it  interacBve  -­‐  details  on  demand.   Help  people  find  things  in  your  network!  (Search?)   Show  more  on  hover/click To o m uch d ata =Not alw ays go o d d ata science!
More  Reminders! Different  layouts  communicate  different  things  to   your  viewer  -­‐  choose  wisely.   Take  care:  people  will  infer  things  from  proximity/similarity   even  if  it  was  not  intended!   Consider  if  it  has  to  be  a  “network”  display  at  all:  Is  it  the  stats   you  care  about?    The  important  nodes  who  branch  sub-­‐ communiBes?    The  ones  with  the  most  in/out  links?    Graph   those  instead. 65
The  Map  is  Not  the  Territory… Forest  Pi6s  (thanks  to  Noah  Friedkin)  h6p://www.analyBctech.com/networks/pi6s.htm
The  Map  is  Not  the  Territory… Forest  Pi6s  (thanks  to  Noah  Friedkin)  h6p://www.analyBctech.com/networks/pi6s.htm
Thanks! @arnicas lynn@ghostweather.com
68 http://www.ghostweather.com/workshops/nyc_visdata.html lynn @ ghostweather.com Dec 11-12, NYC
A  Few  More  References Jeff  Heer  class  slides:  h6p://hci.stanford.edu/ courses/cs448b/w09/lectures/20090204-­‐ GraphsAndTrees.pdf   A  great  in-­‐progress  book  on  networks:  h6p:// barabasilab.neu.edu/networksciencebook/   Mark  Newman’s  new  book-­‐  NetWorks:  An   IntroducBon   Eyeo  FesBval  videos  from  Moritz  Stefaner,   Manuel  Lima,  Stefanie  Posavec   Journal  of  Graph  Algorithms  and  ApplicaBons:   h6p://jgaa.info/home.html     Jim  Vallandingham’s  D3  network  tutorials:   h6p://flowingdata.com/2012/08/02/how-­‐to-­‐ make-­‐an-­‐interacBve-­‐network-­‐visualizaBon/,   h6p://vallandingham.me/ bubble_charts_in_d3.html   Brian  Keegan’s  post  about  GamerGate  tweets   and  the  dangers  of  network  vis   Robert  Kosara’s  post:  h6p:// eagereyes.org/techniques/graphs-­‐hairball   Lane  Harrison’s  post:  h6p://blog.visual.ly/ network-­‐visualizaBons/   MS  Lima’s  book  Visual  Complexity   Jason  Sundram’s  tool  to  drive  Gephi  layout   from  command  line:  h6ps://github.com/ jsundram/pygephi     A  couple  arBcles  on  community  structure:   Overlapping  Community  DetecBon  in   Networks:  State  of  the  Art  and   ComparaBve  Study  by  Jierui  Xie,   Stephen  Kelley,  Boleslaw  K.   Szymanski   Empirical  Comparison  of  Algorithms  for   Network  Community  DetecBon  by   Leskovec,  Lang,  Mahoney   My  posts  on  NetworkX  with  D3  and   Twi6er  network  vis  with  Gephi

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Visualizing Networks

  • 2. The  Hairball:  A  Metaphor  for   Complexity h6p://www.nd.edu/~networks/PublicaBon%20Categories/01%20Review%20ArBcles/ScaleFree_ScienBfic%20Ameri%20288,%2060-­‐69%20(2003).pdf
  • 4. Release by Joint Economic Committee minority Republicans led by Boehner (2010), Iliinsky & Steele fig 4-14 4
  • 5. Release by Joint Economic Committee minority Republicans led by Boehner (2010), Iliinsky & Steele fig 4-14 Neither Exploratory Nor Explanatory: Political. 4
  • 6. Redesign by Robert Palmer (Iliisnky & Steele fig 4-15) 5
  • 7. Redesign by Robert Palmer (Iliisnky & Steele fig 4-15) “By releasing your chart, instead of meaningfully educating the public, you willfully obfuscated an already complicated proposal.There is no simple proposal to solve this problem.You instead chose to shout ‘12! 16! 37! 9! 24!’ while we were trying to count something.” 5
  • 8. WHAT  IS  A  NETWORK? It’s  not  a  visualizaBon.    Think  of  it  as  a  data  structure.
  • 9. Data  relaBonship:  enBBes  +  relaBonships  to  other   objects  (node/edge,  vertex/link)   Nodes  and  Edges  may  have  a6ributes,  eg.   gender,  age,  weight,  tv  prefs   connecBon  date,  frequency  of  contact,  type  of  exchange,   direcBonality  of  relaBonship   a6ributes  may  be  calculated  from  network  relaBons  too
  • 13. 9h6p://blog.visual.ly/network-­‐visualizaBons/ 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),
  • 14. 10
  • 15. 10
  • 16. 10
  • 17. 10 It’s  a  natural  human  trait  to  see  visual  similarity  and  proximity  as  meaningful.   Be  very  careful  about  your  display  choices  and  layout  methods!  
  • 18. Reading  a  network  visualizaBon There’s obviously something important going on here, structurally....
  • 19. Reading  a  network  visualizaBon Look at this outlier case! There’s obviously something important going on here, structurally....
  • 20. Reading  a  network  visualizaBon Look at this outlier case! There’s obviously something important going on here, structurally.... A ménage à trois over here
  • 21. Reading  a  network  visualizaBon Look at this outlier case! There’s obviously something important going on here, structurally.... A ménage à trois over here MIS? Using  a  “random”  Gephi  layout  on  the  dolphins
  • 22. Reading  a  network  visualizaBon Look at this outlier case! There’s obviously something important going on here, structurally.... A ménage à trois over here MIS? Using  a  “random”  Gephi  layout  on  the  dolphins Random!
  • 23. 12 TOOLS  AND  CONCEPTS  FOR  TODAY CreaBng  network  layouts...
  • 24. 13 Gephi  (and  a  li6le  D3)
  • 25. J  BerBn:  Semiology  
 of  Graphics BerBn,  J.  Semiology  of  Graphics:  Diagrams,     Networks,  Maps  (1967) Linear   Circular   Irregular   Regular  (Tree)   3D   Matrix  /   BiparBte
  • 26. J  BerBn:  Semiology  
 of  Graphics BerBn,  J.  Semiology  of  Graphics:  Diagrams,     Networks,  Maps  (1967) Linear   Circular   Irregular   Regular  (Tree)   3D   Matrix  /   BiparBte
  • 27. Algorithmic  Approaches Frank  van  Ham  talk  slides:  h6p://bit.ly/s6udpy
  • 28. MATRIX  LAYOUTS  /  REPRESENTATIONS
  • 30. Real  social  networks  are   generally  quite  sparse. h6p://www.cise.ufl.edu/research/sparse/matrices/Newman/dolphins.html
  • 31. D3  demo  by  me  h6p://www.ghostweather.com/essays/talks/networkx/adjacency.html
  • 32. D3  demo  by  me  h6p://www.ghostweather.com/essays/talks/networkx/adjacency.html
  • 33. 20 Readability:  Matrix  Be6er  (except  for   path  finding) Jean-Daniel Fekete http://lamut.informatik.uni-wuerzburg.de/gd2014/data/uploads/gd2014-fekete-keynote.pdf
  • 34. ARC  /  LINEAR  LAYOUTS
  • 35. Philipp  Steinweber  and  Andreas  Koller   Similar  Diversity,  2007 For  a  D3  example  in  another  domain:  h6p://tradearc.laserdeathstehr.com/
  • 36. Topics  in  Da  Vinci  Code  (by  me) 23 https://www.dropbox.com/s/090vwj12kh3mu96/Screenshot %202014-11-04%2020.45.02.png?dl=0 Lynn Cherny - http://www.ghostweather.com/essays/talks/openvisconf/topic_arc_diagram/TopicArc.html
  • 37. Topics  in  Da  Vinci  Code  (by  me) 23 https://www.dropbox.com/s/090vwj12kh3mu96/Screenshot %202014-11-04%2020.45.02.png?dl=0 Lynn Cherny - http://www.ghostweather.com/essays/talks/openvisconf/topic_arc_diagram/TopicArc.html
  • 39. CIRCULAR  /  CHORD  LAYOUTS
  • 40. h6p://circos.ca/images/ Circos “If it's round, Circos can probably do it”
  • 41. h6p://circos.ca/images/ Circos “If it's round, Circos can probably do it” TIL (shocking): It’s all perl code?!
  • 44. A  Nice  UI  Improvement  -­‐  DifferenBate  Source/ DesBnaBon 28 Nikola Sander, http://www.global-migration.info/
  • 45. Hierarchical  Edge  Bundling  demo  by   @mbostock D3:  h6p://bl.ocks.org/1044242 "Hierarchical  Edge  Bundles:  VisualizaBon  of  Adjacency  RelaBons  in  Hierarchical  Data”,  Danny  Holten,  IEEE  TransacBons  on  VisualizaBon   and  Computer  Graphics  (TVCG;  Proceedings  of  Vis/InfoVis  2006),  Vol.  12,  No.  5,  Pages  741  -­‐  748,  2006.
  • 46. SIMPLE  CALCULATIONS  ON  NETWORKS   CAN  TELL  YOU  LOADS Oven  you  need  to  visualize  the  structure/role  of  the  graph  elements  as   part  of  the  visualizaBon:  So,  do  some  simple  math.
  • 47. 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   preferenBal  a6achment  to  the   popular  kids. h6p://mlg.ucd.ie/files/summer/tutorial.pdf
  • 48. The  Threat  of  Hub-­‐Loss Albert-­‐László  Barabási  and  Eric  Bonabeau,  Scale-­‐Free  Networks,  2003.h6p://www.scienBficamerican.com/arBcle.cfm?id=scale-­‐free-­‐ networks
  • 49. VisualizaBon  Aside:  If  Some  Names  are  Huge,   the  Others  are  Invisible-­‐? 33 CorrecBng  for  text  size  by  degree  display  issue
  • 50. VisualizaBon  Aside:  If  Some  Names  are  Huge,   the  Others  are  Invisible-­‐? 33 Gephi  Panel CorrecBng  for  text  size  by  degree  display  issue
  • 51. VisualizaBon  Aside:  If  Some  Names  are  Huge,   the  Others  are  Invisible-­‐? 33 Gephi  Panel CorrecBng  for  text  size  by  degree  display  issue
  • 52. VisualizaBon  Aside:  If  Some  Names  are  Huge,   the  Others  are  Invisible-­‐? 33 Gephi  Panel CorrecBng  for  text  size  by  degree  display  issue
  • 53. 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   communiBes.” h6p://en.wikipedia.org/wiki/Centrality#Betweenness_centrality Lusseau  and  Newman.  h6p://www.ncbi.nlm.nih.gov/pmc/arBcles/ PMC1810112/pdf/15801609.pdf
  • 54. 35 Judging  By  Eye  Will  Probably  Be  Wrong...
  • 55. 35 ? This one? Judging  By  Eye  Will  Probably  Be  Wrong...
  • 56. 35 ? This one? Sized  by  Betweenness Judging  By  Eye  Will  Probably  Be  Wrong...
  • 57. Eigenvector  Centrality IntuiBon:  A  node  is  important  if  it  is  connected  to   other  important  nodes   A  node  with  a  small  number  of  influenBal  contacts   may  outrank  one  with  a  larger  number  of  mediocre   contacts h6p://mlg.ucd.ie/files/summer/tutorial.pdf h6p://demonstraBons.wolfram.com/NetworkCentralityUsingEigenvectors/
  • 59. Community  DetecBon  Algorithms E.g.,  the  Louvain  method,  in  Gephi  as  “Modularity.”       Many  layout  algorithms  help  you  intuit  these   structures,  but  don’t  rely  on  percepBon  of  layout! h6p://en.wikipedia.org/wiki/File:Network_Community_Structure.png
  • 60. You  can  even  do  it  in  your  browser  now… 39 http://bl.ocks.org/john-guerra/ecdde32ab4ad91a1a240
  • 61. You  can  even  do  it  in  your  browser  now… 39 http://bl.ocks.org/john-guerra/ecdde32ab4ad91a1a240
  • 62. CitaBon:  Lusseau  D  (2007)  Why  Are  Male  Social  RelaBonships  Complex  in  the  Doub|ul  Sound  Bo6lenose  Dolphin   PopulaBon?.  PLoS  ONE  2(4):  e348.  doi:10.1371/journal.pone.0000348
  • 63. IdenBfying  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
  • 64. IdenBfying  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
  • 65. IdenBfying  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
  • 66. IdenBfying  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
  • 67. IdenBfying  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
  • 68. IdenBfying  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
  • 69. Eduarda  Mendes  Rodrigues,  Natasa  Milic-­‐Frayling,  Marc  Smith,  Ben  Shneiderman,  Derek  Hansen,  Group-­‐in-­‐a-­‐box  Layout  for  MulB-­‐ faceted  Analysis  of  CommuniBes.  IEEE  Third  InternaBonal  Conference  on  Social  CompuBng,  October  9-­‐11,  2011.  Boston,  MA
  • 72. ALGORITHMIC  LAYOUTS   Gephi  /  D3.js  /  Other  tools
  • 73. Gephi  à  Sigma.js  /  D3 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  with  Java  or  Python  (I  have  code  for   this)   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  site  export   Also  offers  a  force-­‐directed  layout  plugin  for  graphs  without  x&y  coords   Does  CANVAS  drawing,  not  SVG    Export  JSON  from  Gephi  and  load  into  D3  network  layout
  • 75. Sigma.js  version  of  the  Gephi  export  h6p://exploringdata.github.com/vis/human-­‐disease-­‐network/ Movie:
  • 76. Sigma.js  version  of  the  Gephi  export  h6p://exploringdata.github.com/vis/human-­‐disease-­‐network/ Movie:
  • 77. Sample  Layout  Plugins  in  Gephi h6ps://gephi.org/tutorials/gephi-­‐tutorial-­‐layouts.pdf
  • 78. Gephi  Plugin  Layout  Details Layout Complexity Graph  Size Author Comment Circular O(N) 1  to  1M  nodes Ma6  Groeninger Used  to  show   distribuBon,  ordered   layout Radial  Axis O(N) 1  to  1M  nodes Ma6  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     Jacomy (does  use  weight) OpenOrd O(N*log(N)) 100  to  1M  nodes S.  MarBn,  W.  M.  Brown,   R.  Klavans,  and  K.  Boyack Focus  on  clustering  (uses   edge  weight) Yifan  Hu   O(N*log(N)) 100  to  100K  nodes Yifan  Hu (no  edge  weight) Fruchterman-­‐Rheingold O(N²) 1  to  1K  nodes Fruchterman  &   Rheingold! ParBcle  system,  slow  (no   edge  weight) GeoLayout O(N) 1  to  1M  nodes Alexis  Jacomy Uses  Lat/Long  for  layout h6ps://gephi.org/2011/new-­‐tutorial-­‐layouts-­‐in-­‐gephi/
  • 79. Dolphins  Again 50 OpenOrd  +  “No  Overlap” ForceAtlas2
  • 80. Dolphins  Again 50 OpenOrd  +  “No  Overlap” ForceAtlas2
  • 81. Dolphins  Again 50 OpenOrd  +  “No  Overlap” ForceAtlas2
  • 82. 51 Weight  2:  Force  Atlas Weight  4:  Force  Atlas Unweighted   dolphins,   Force  Atlas
  • 83. 51 Weight  2:  Force  Atlas Weight  4:  Force  Atlas Weight  4:  Yifan  Hu Unweighted   dolphins,   Force  Atlas
  • 86. 53 FINAL  DESIGN  THOUGHTS…   BY  HAND?  BY  ALGORITHM-­‐?
  • 88. Conspiracy  Theorist  Mark  Lombardi Learning  from  Lombardi:   h6p://benfry.com/exd09/
  • 89. Conspiracy  Theorist  Mark  Lombardi Learning  from  Lombardi:   h6p://benfry.com/exd09/
  • 90. J.  BerBn  Again… BerBn,  J.  Semiology  of  Graphics:  Diagrams,  Networks,  Maps  (1967)
  • 91. Age Groups and Movement to/from Paris to Rural Spots
  • 92. (P)  Paris   (Z)  Paris  Suburbs   (+50)  Communes  of  >50K   (+10)  Communes  of  >10K   (-­‐10)  Communes  of  <10K   (R)  Rural
  • 93. Ger  Hobbelt  in  D3:  h6p://bl.ocks.org/3637711 Hybrid  Method:  Use  algorithmic  layout,  and   then  adjust  nodes  by  hand.
  • 94. Minimize  Info:  Less  is  More 60 Color by important data value, sized by degree, no edges
  • 97. Brian  Keegan’s  “15  Minutes  of  Fame  as  a  B-­‐List   GamerGate  Celebrity” 62 http://www.brianckeegan.com/2014/10/my-15-minutes-of-fame-as-a-b-list-gamergate-celebrity/
  • 98. Brian  Keegan’s  “15  Minutes  of  Fame  as  a  B-­‐List   GamerGate  Celebrity” 62 http://www.brianckeegan.com/2014/10/my-15-minutes-of-fame-as-a-b-list-gamergate-celebrity/
  • 99. Brian  Keegan’s  “15  Minutes  of  Fame  as  a  B-­‐List   GamerGate  Celebrity” 62 http://www.brianckeegan.com/2014/10/my-15-minutes-of-fame-as-a-b-list-gamergate-celebrity/ Actually:
  • 100. Wrap it up on design...
  • 101. Reminders Do  data  analysis  /  reducBon  -­‐  why  would  you   want  to  show  1T  of  network  data?   Calculate  and  reveal  important  facts  about   node  relaBonships.   Consider  your  visual  encodings:     –visualizaBon,  not  data  vomit.   Make  it  interacBve  -­‐  details  on  demand.   Help  people  find  things  in  your  network!  (Search?)   Show  more  on  hover/click
  • 102. Reminders Do  data  analysis  /  reducBon  -­‐  why  would  you   want  to  show  1T  of  network  data?   Calculate  and  reveal  important  facts  about   node  relaBonships.   Consider  your  visual  encodings:     –visualizaBon,  not  data  vomit.   Make  it  interacBve  -­‐  details  on  demand.   Help  people  find  things  in  your  network!  (Search?)   Show  more  on  hover/click To o m uch d ata =Not alw ays go o d d ata science!
  • 103. More  Reminders! Different  layouts  communicate  different  things  to   your  viewer  -­‐  choose  wisely.   Take  care:  people  will  infer  things  from  proximity/similarity   even  if  it  was  not  intended!   Consider  if  it  has  to  be  a  “network”  display  at  all:  Is  it  the  stats   you  care  about?    The  important  nodes  who  branch  sub-­‐ communiBes?    The  ones  with  the  most  in/out  links?    Graph   those  instead. 65
  • 104. The  Map  is  Not  the  Territory… Forest  Pi6s  (thanks  to  Noah  Friedkin)  h6p://www.analyBctech.com/networks/pi6s.htm
  • 105. The  Map  is  Not  the  Territory… Forest  Pi6s  (thanks  to  Noah  Friedkin)  h6p://www.analyBctech.com/networks/pi6s.htm
  • 108. A  Few  More  References Jeff  Heer  class  slides:  h6p://hci.stanford.edu/ courses/cs448b/w09/lectures/20090204-­‐ GraphsAndTrees.pdf   A  great  in-­‐progress  book  on  networks:  h6p:// barabasilab.neu.edu/networksciencebook/   Mark  Newman’s  new  book-­‐  NetWorks:  An   IntroducBon   Eyeo  FesBval  videos  from  Moritz  Stefaner,   Manuel  Lima,  Stefanie  Posavec   Journal  of  Graph  Algorithms  and  ApplicaBons:   h6p://jgaa.info/home.html     Jim  Vallandingham’s  D3  network  tutorials:   h6p://flowingdata.com/2012/08/02/how-­‐to-­‐ make-­‐an-­‐interacBve-­‐network-­‐visualizaBon/,   h6p://vallandingham.me/ bubble_charts_in_d3.html   Brian  Keegan’s  post  about  GamerGate  tweets   and  the  dangers  of  network  vis   Robert  Kosara’s  post:  h6p:// eagereyes.org/techniques/graphs-­‐hairball   Lane  Harrison’s  post:  h6p://blog.visual.ly/ network-­‐visualizaBons/   MS  Lima’s  book  Visual  Complexity   Jason  Sundram’s  tool  to  drive  Gephi  layout   from  command  line:  h6ps://github.com/ jsundram/pygephi     A  couple  arBcles  on  community  structure:   Overlapping  Community  DetecBon  in   Networks:  State  of  the  Art  and   ComparaBve  Study  by  Jierui  Xie,   Stephen  Kelley,  Boleslaw  K.   Szymanski   Empirical  Comparison  of  Algorithms  for   Network  Community  DetecBon  by   Leskovec,  Lang,  Mahoney   My  posts  on  NetworkX  with  D3  and   Twi6er  network  vis  with  Gephi