A  project  from  the  Social  Media  Research  Founda8on:  h:p://www.smrfounda8on.org  

Network  Maps  for  End  Users:    

Collect,  Analyze,  Visualize  and  Communicate  

Network  Insights  with  Zero  Coding  

About  Me  

Introduc8ons    Marc  A.  Smith  Chief  Social  Scien8st  Connected  Ac8on  Consul8ng  Group    Marc@connectedac8on.net  h:p://www.connectedac8on.net  h:p://www.codeplex.com/nodexl  h:p://www.twi:er.com/marc_smith  h:p://delicious.com/marc_smith/Paper    h:p://www.flickr.com/photos/marc_smith  h:p://www.facebook.com/marc.smith.sociologist  h:p://www.linkedin.com/in/marcasmith  h:p://www.slideshare.net/Marc_A_Smith  h:p://www.smrfounda8on.org    

h:p://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/  

h:p://www.flickr.com/photos/amycgx/3119640267/  

•  Central  tenet      –  Social  structure  emerges  from    –    the  aggregate  of  rela8onships  (8es)    –    among  members  of  a  popula8on  

•  Phenomena  of  interest  –  Emergence  of  cliques  and  clusters    –    from  pa:erns  of  rela8onships  –  Centrality  (core),  periphery  (isolates),    –    betweenness  

•  Methods  –  Surveys,  interviews,  observa8ons,    

log  file  analysis,  computa8onal    analysis  of  matrices    

(Hampton  &Wellman,  1999;  Paolillo,  2001;  Wellman,  2001)  

Source:  Richards,  W.  (1986).  The  NEGOPY  network  analysis  program.  Burnaby,  BC:  Department  of    Communica8on,  Simon  Fraser  University.  pp.7-­‐16

Social  Network  Theory  h:p://en.wikipedia.org/wiki/Social_network  

SNA  101  •  Node  

–  “actor”  on  which  rela8onships  act;  1-­‐mode  versus  2-­‐mode  networks  •  Edge  

–  Rela8onship  connec8ng  nodes;  can  be  direc8onal  •  Cohesive  Sub-­‐Group  

–  Well-­‐connected  group;  clique;  cluster  •  Key  Metrics  

–  Centrality  (group  or  individual  measure)  •  Number  of  direct  connec8ons  that  individuals  have  with  others  in  the  group  (usually  look  at  incoming  connec8ons  only)  

•  Measure  at  the  individual  node  or  group  level  –  Cohesion  (group  measure)  

•  Ease  with  which  a  network  can  connect  •  Aggregate  measure  of  shortest  path  between  each  node  pair  at  network  level  reflects  average  distance  

–  Density  (group  measure)  •  Robustness  of  the  network  •  Number  of  connec8ons  that  exist  in  the  group  out  of  100%  possible    

–  Betweenness  (individual  measure)  •  #  shortest  paths  between  each  node  pair  that  a  node  is  on  •  Measure  at  the  individual  node  level  

•  Node  roles  –  Peripheral  –  below  average  centrality  –  Central  connector  –  above  average  centrality  –  Broker  –  above  average  betweenness  

E  

D  

F  

A  

C  B  

H  

G  

I  

C  D  

E  

A   B   D   E  

Email  (and  more)  is    from  people  to  people   8  

PaDerns  are  leE  behind  

9  

There  are  many  kinds  of  8es….  

h:p://www.flickr.com/photos/stevendepolo/3254238329  

World  Wide  Web  

Each  contains  one  or  more  social  networks  

Welser,  Howard  T.,  Eric  Gleave,  Danyel  Fisher,  and  Marc  Smith.  2007.  Visualizing  the  Signatures  of  Social  Roles  in  Online  Discussion  Groups.    The  Journal  of  Social  Structure.  8(2).  

Experts  and  “Answer  People”  

Discussion  starters,  Topic  se:ers  

Discussion  people,  Topic  se:ers  

Tag  Ecologies  I  

Adamic  et  al.  WWW  2008  

HUB-­‐AND-­‐SPOKE  OF  DECEIT:  When  Enron  employees  communicated  about  legi8mate  projects,  e-­‐mails  were  reciprocal  and  informa8on  was  shared  widely  (right),  but  communica8ons  about  an  illicit  project  (les)  reveal  a  sparse  network  with  a  central,  informed  clique  and  isolated  external  players.  Brandy  Aven,  CMU  h:p://www.sciencenews.org/view/generic/id/330731/8tle/Informa8on_flow_can_reveal_dirty_deeds  

Networks  reveal  pa:erns  

Goal:  Make  SNA  easier  

•  Exis8ng  Social  Network  Tools  are  challenging  for  many  novice  users  

•  Tools  like  Excel  are  widely  used  •  Leveraging  a  spreadsheet  as  a  host  for  SNA  lowers  barriers  to  network  data  analysis  and  display  

Who  we  are  People   Disciplines   InsGtuGons  

University  Faculty  

Computer  Science   University  of  Maryland  

Students   HCI,  CSCW   Oxford  Internet  Ins8tute  

Industry     Machine  Learning   Stanford  University  

Independent     Informa8on  Visualiza8on  

Microsos  Research  

Researchers     UI/UX   Illinois  Ins8tute  of  Technology  

Developers     Social  Science/Sociology   Connected  Ac8on  

Network  Analysis     Cornell  

Collec8ve  Ac8on     Morningside  Analy8cs  

What  we  are  trying  to  do:  Open  Tools,  Open  Data,  Open  Scholarship  •  Build  the  “Firefox  of  GraphML”  –  open  tools  for  collec8ng  and  visualizing  social  media  data  

•  Connect  users  to  network  analysis  –  make    network  charts  as  easy  as  making  a  pie  chart  

•  Connect  researchers  to  social  media  data  sources  •  Archive:  Be  the  “Allen  Very  Large  Telescope  Array”  for  Social  Media  data  –  coordinate  and  aggregate  the  results  of  many  user’s  data  collec8on  and  analysis  

•  Create  open  access  research  papers  &  findings  •  Make  “collec3ons  of  connec3ons”  easy  for  users  to  manage    

What  we  have  done:  Open  Tools  

•  NodeXL  •  Data  providers  (“spigots”)  –  ThreadMill  Message  Board  –  Exchange  Enterprise  Email  –  Voson  Hyperlink  –  SharePoint  –  Facebook  –  Twi:er  –  YouTube  –  Flickr  

What  we  have  done:  Open  Data  •  NodeXLGraphGallery.org  – User  generated  collec8on  of  network  graphs,  datasets  and  annota8ons  

–  Collec8ve  repository  for  the  research  community  

–  Published  collec8ons  of  data  from  a  range  of  social  media  data  sources  to  help  students  and  researchers  connect  with  data  of  interest  and  relevance  

What  we  have  done:  Open  Scholarship  

What  we  have  done:  Open  Scholarship  

Social  Media  Research  Founda8on  h3p://smrfounda:on.org  

Heather  has  high  

betweenness  

NodeXL  Network  Overview  Discovery  and  ExploraGon  add-­‐in  for  Excel  2007/2010  

A  minimal  network  can  illustrate  the  ways  different  

loca8ons  have  different  values  for  centrality  and  degree  

Now  Available  

h:p://www.flickr.com/photos/marc_smith/sets/72157622437066929/  

h:p://www.connectedac8on.net/2010/04/25/bernie-­‐hogans-­‐facebook-­‐social-­‐network-­‐data-­‐provider-­‐and-­‐visualiza8on-­‐toolkit/  

Network  of  connec8ons  among  the  people  who  tweeted  the  term    “PAWCON”  on  19  October  2011  

NodeXL  data  import  sources  

Example  NodeXL  data  importer  for  Twi:er  

NodeXL  imports  “edges”  from  social  media  data  sources  

NodeXL  Automa8on  makes  analysis  simple  and  fast  

NodeXL  Network  Metrics  

NodeXL  simplifies  mapping  data  a:ributes  to  display  a:ributes  

NodeXL  Generates  “Sub-­‐Graph”  Images  

NodeXL  displays  subgraph  images  along  with  network  metadata  

NodeXL  allows  for  fine  control  over  the  display  of  the  network  

NodeXL  Generates  Images  of  Networks  

NodeXL  Generates  Network  Graph  Images  

NodeXL  enables  filtering  of  networks  

NodeXL  Generates  Filtered  Network  Images  

Analogy:  Clusters  Are  Occluded  Hard  to  count  nodes,  clusters  

Separate  Clusters  Are  More  Comprehensible  

TwiDer  Network  for  “MicrosoE  Research”  

NodeXL  Generates  Overall  Network  Metrics  

Social  networks  in  TwiDer  among  people  with  at  least  one  connecGon  to  someone  else  who  Tweeted  “Obama”  on  January  25,  2011    

Network  of  word  pairs  frequently  men8ons  among  people  who  Tweeted  the  name  “Obama”  on  January  25,  2011    

US  Congressman  Paul  Ryan  word  network  (January  22,  2011)  

Congresswoman  Michel  Bachmann  keyword  network  (January  25,  2011)    

What  we  want  to  do:    (Build  the  tools  to)  map  the  social  web  •  Move  NodeXL  to  the  web:  

–  Node  for  Google  Doc  Spreadsheets!  –  WebGL  Canvas  

•  Connect  to  more  data  sources  of  interest:  –  RDF,  MediaWikis,  Gmail,  NYT,  Cita8on  Networks  

•  Solve  hard  network  manipula8on  UI  problems:  –  Modal  transform,  Time  series,  Automated  layouts  

•  Grow  and  maintain  archives  of  social  media  network  data  sets  for  research  use.  

•  Improve  network  science  educa8on:  –  Workshops  on  social  media  network  analysis  –  Live  lectures  and  presenta8ons  –  Videos  and  training  materials  

Work  Items  Autofill  Group  A:ribute  Merge  Edges  by  A:ribute  Modal  Transform  Merge  Workbooks  Automated  Dynamic  Filters:  Time  Series  Analysis,  contrast  Cap8ons  and  Legends  Upload  to  Graph  Gallery++:  cap8ons,  workbook  Graph  Gallery++    

User  Accounts,  Repor8ng,  RSS  Feeds,    Network  Visualiza8on  Web  Canvas  

Import:  RDF,  Wiki,  SharePoint,  Keyword  networks  from  text  Metrics:  Triad  Census  Layouts:    

Force  Atlas  2,  Lin  Log,  “Bakshy  Plots”,  Quality  Measures  Query-­‐by-­‐example  search  for  network  structures  

How  you  can  help  

•  Sponsor  a  feature  •  Sponsor  Webshop  2012  •  Sponsor  a  student  •  Schedule  training  •  Sponsor  the  founda8on  •  Donate  your  money,  code,  computa8on,  storage,  bandwidth,  data  or  employee’s  8me  

•  Help  promote  the  work  of  the  Social  Media  Research  Founda8on  

Contact:  

   Marc  A.  Smith  Chief  Social  Scien8st  Connected  Ac8on  Consul8ng  Group    Marc@connectedac8on.net  h:p://www.connectedac8on.net  h:p://www.codeplex.com/nodexl  h:p://www.twi:er.com/marc_smith  h:p://delicious.com/marc_smith/Paper    h:p://www.flickr.com/photos/marc_smith  h:p://www.facebook.com/marc.smith.sociologist  h:p://www.linkedin.com/in/marcasmith  h:p://www.slideshare.net/Marc_A_Smith  h:p://www.smrfounda8on.org    

A  project  from  the  Social  Media  Research  Founda8on:  h:p://www.smrfounda8on.org  

Network  Maps  for  End  Users:    

Collect,  Analyze,  Visualize  and  Communicate  

Network  Insights  with  Zero  Coding