1.
Charting Collections of Social
Media Connections with NodeXL
Maps and reports for social media networks
April 10-12, Chicago, IL

2.
Please silence
cell phones
April 10-12, Chicago, IL

3.
About Me
Marc A. Smith
Chief Social Scientist
Connected Action Consulting Group
Marc@connectedaction.net
http://www.connectedaction.net
http://www.codeplex.com/nodexl
http://www.twitter.com/marc_smith
http://delicious.com/marc_smith/Paper
http://www.flickr.com/photos/marc_smith
http://www.facebook.com/marc.smith.sociologist
http://www.linkedin.com/in/marcasmith
http://www.slideshare.net/Marc_A_Smith April 10-12, Chicago, IL
http://www.smrfoundation.org

4.
http://smrfoundation.org
April 10-12, Chicago, IL

5.
Social Media (email,
Facebook, Twitter,
YouTube, and more)
is all about
connections
from people
to people.
5
5

6.
Patterns are
left behind
6
6

7.
There are many kinds of ties…. Send, Mention, Like, Link,
Reply, Rate, Review, Favorite, Friend, Follow, Forward, Edit, Tag, Comment, Check-in…
http://www.flickr.com/photos/stevendepolo/3254238329

8.
Strong ties

9.
Weak ties

10.
http://www.flickr.com/photos/fullaperture/81266869/
Strength of Weak ties

11.
“Think Link”
Nodes & Edges
Is related to
A B
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12.
Each contains one or more
social networks
World Wide Web

13.
A network is born whenever two GUIDs are joined.
Username Attributes Username Attributes
@UserName1 Value, value @UserName2 Value, value
A B
Vertex1 Vertex 2 “Edge” “Vertex1” “Vertex2”
Attribute Attribute Attribute
@UserName1 @UserName2 value value value

14.
NodeXL imports “edges” from social media data sources

15.
http://byobi.com/blog/2013/03/analyzing-sql-server-object-dependencies-with-nodexl/
Bill Anton (@SQLbyoBI)
HOW TO BUILD
a table of all the object dependencies in a database.
15

16.
Social
Networks
Jacob Moreno ’ s early
social network diagram of
History:
positive and negative
relationships among
from the dawn of time!
members of a football
team.
Theory and method: Originally published in
1934 ->
Moreno, J. L. (1934). Who
shall survive? Washington,
Jacob L. Moreno
DC: Nervous and Mental
Disease Publishing
Company.
http://en.wikipedia.org/wiki/Jacob_L._Moren 16

17.
Social network diagram of relationships among workers in a factory illustrates the positions
different workers occupy within the workgroup.
Originally published in Roethlisberger, F., and Dickson, W. (1939). Management and
the worker. Cambridge, UK: Cambridge University Press.

18.
Location, Location, Location

19.
Position, Position, Position

20.
Introduction to NodeXL
Like MSPaint™ for network graphs.
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21.
Communities in
Cyberspace

22.
http://www.flickr.com/photos/badgopher/3264760070/

23.
http://www.flickr.com/photos/druclimb/2212572259/in/photostrea
m/

24.
http://www.flickr.com/photos/hchalkley/47839243/

25.
http://www.flickr.com/photos/rvwithtito/4236716778

26.
http://www.flickr.com/photos/62693815@N03/6277208708/

27.
Social Network Maps Reveal
Key influencers in any topic.
Sub-groups.
Bridges.

28.
Hubs

29.
Bridges

30.
http://www.flickr.com/photos/storm-

31.
http://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/

32.
http://www.flickr.com/photos/amycgx/3119640267/

33.
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 & Discussion people
Discussion starters
“Answer People” Topic setters
Topic setters
41

34.
NodeXL: Network Overview Discovery and Exploration add-in for Excel 2007/2010
A minimal network can illustrate
the ways different locations
have different values for
centrality and degree
42

35.
http://www.newscientist.com/blogs/onepercent/2011/11/occupy-vs-tea-party-what-their.html
#teaparty
15 November 2011
#occupywallstreet
15 November 2011

36.
6 kinds of Twitter social media networks
45

37.
#My2K
Polarized 46

38.
#CMgrChat
In-group / Community
47

39.
Lumia
Brand / Public Topic
48

40.
#FLOTUS
Bazaar
49

41.
New York Times Article
Paul Krugman
Broadcast: Audience + Communities 50

42.
Dell Listens/Dellcares
Support
51

43.
SNA questions for social media:
1. What does my topic network look like?
2. What does the topic I aspire to be look like?
3. What is the difference between #1 and #2?
4. How does my map change as I intervene?
What do #SQLPass and #PASSBAC look like?
52

44.
http://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=3965
Top 10 Vertices, Ranked by Betweenness Centrality:
@sqlpass
@BrentO
@PaulRandal
@ClerisyDatabase
@SQLRockstar
@jenstirrup
@SQLChicken
@SQLSocialite
@MicrosoftBI
@kekline

45.
http://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=3966
Top 10 Vertices, Ranked by Betweenness
Centrality:
@passbac
@MicrosoftBI
@dennylee
@impetustech
@sqlpass
@ExtendedResults
@StaciaMisner
@marcorus
@SQLRockstar
@jenstirrup

46.
http://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=3982
Top 10 Vertices, Ranked by Betweenness Centrality:
@SQLServer
@eric_kavanagh
@DBA_MAN
@confio
@DZone
@SQLRockstar
@YvesMulkers
@BrentO
@SQL_By_Joey
@zymasesystems

47.
http://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=3983
Top 10 Vertices, Ranked by Betweenness Centrality:
@timoreilly
@hortonworks
@cloudera
@YvesMulkers
@TDWI
@IBMbigdata
@eric_kavanagh
@furrier
@benjguin
@andreisavu

48.
http://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=3984
Top 10 Vertices, Ranked by Betweenness Centrality:
@neo4j
@peterneubauer
@emileifrem
@jimwebber
@DZone
@Neo4jFr
@al3xandru
@volkantufekci
@ajlopez
@p3rnilla

49.
Social Network Theory
http://en.wikipedia.org/wiki/Social_network
Central tenet
Social structure emerges from the aggregate of relationships (ties)
among members of a population
Phenomena of interest
Emergence of cliques and clusters from patterns of relationships
Centrality (core), periphery (isolates), betweenness Source: Richards, W.
(1986). The NEGOPY
network analysis
Methods program. Burnaby, BC:
Department of
Surveys, interviews, observations, log file analysis, computational Communication, Simon
analysis of matrices Fraser University. pp.7-
16
(Hampton &Wellman, 1999; Paolillo, 2001; Wellman, 2001)
59

50.
SNA 101 • Node
– “actor” on which relationships act; 1-mode versus 2-mode networks
• Edge
A – Relationship connecting nodes; can be directional
• Cohesive Sub-Group
– Well-connected group; clique; cluster A B D E
B C • Key Metrics
– Centrality (group or individual measure)
• Number of direct connections that individuals have with others in the group (usually look at
D incoming connections only)
• Measure at the individual node or group level
E – 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 connections that exist in the group out of 100% possible
F G – Betweenness (individual measure)
• # shortest paths between each node pair that a node is on
• Measure at the individual node level
H I • Node roles
– Peripheral – below average centrality C
– Central connector – above average centrality D
– Broker – above average betweenness E

51.
NodeXL: Free/Open Social Network Analysis add-in for Excel 2007/2010
makes graph theory as easy as a pie chart, with integrated analysis of social
media sources. See: http://nodexl.codeplex.com
61

52.
http://www.youtube.com/watch?v=0M3T65Iw3Ac
NodeXL Video

53.
Goal: Make SNA easier
• Existing 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
63

54.
Twitter Network for “Microsoft Research”
*BEFORE*

55.
Twitter Network for “Microsoft Research”
*AFTER*
65

56.
Network Motif Simplification
Cody Dunne, University of Maryland
66

57.
NodeXL calculates
network metrics and word
pairs
67

58.
The Content summary
spreadsheet displays the most
frequently used URLs, hashtags,
and user names within the
network as a whole and within
each calculated sub-group.
68

59.
69

60.
NodeXL Ribbon in Excel

61.
NodeXL imports “edges” from social media data sources

62.
NodeXL creates a list of “vertices” from imported social media edges
NodeXL displays subgraph images along with network
metadata

63.
NodeXL Perform
collections of
Automation common
operations with
makes analysis a single click
simple and fast

64.
NodeXL Generates Overall Network Metrics

65.
Social Media Research Foundation
People Disciplines Institutions
University Faculty Computer Science University of Maryland
Students HCI, CSCW Oxford Internet Institute
Industry Machine Learning Stanford University
Independent Information Visualization Microsoft Research
Researchers UI/UX Illinois Institute of Technology
Developers Social Science/Sociology Connected Action
Network Analysis Cornell
Collective Action Morningside Analytics
75

66.
What we are trying to do:
Open Tools, Open Data, Open Scholarship
Build the “Firefox of GraphML” – open tools for collecting 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 collection and analysis
Create open access research papers & findings
Make “collections of connections” easy for users to manage
76

67.
What we have done: Open Tools
NodeXL
Data providers (“spigots”)
• ThreadMill Message Board
• Exchange Enterprise Email
• Voson Hyperlink
• SharePoint
• Facebook
• Twitter
• YouTube
• Flickr
77

68.
What we have done: Open Data
NodeXLGraphGallery.org
• User generated collection of network
graphs, datasets and annotations
• Collective repository for the research
community
• Published collections of data from a
range of social media data sources to
help students and researchers connect
with data of interest and relevance
78

69.
What we have done: Open Scholarship
79

70.
What we have done: Open Scholarship
80

71.
What we want to do:
(Build the tools to) map the social web
Move NodeXL to the web: (Node[NOT]XL)
• Node for Google Doc Spreadsheets?
• WebGL Canvas? D3.JS? Sigma.JS
Connect to more data sources of interest:
• RDF, Gmail, NYT, Citation Networks
Solve hard network manipulation UI problems:
• Modal transform, Time series, Automated layouts
Grow and maintain archives of social media network data sets for research use.
Improve network science education:
• Workshops on social media network analysis
• Live lectures and presentations
• Videos and training materials
81

72.
How you can help
Sponsor a feature
Sponsor workshops
Sponsor a student
Schedule training
Sponsor the foundation
Donate your money, code, computation, storage, bandwidth, data or
employee’s time
Help promote the work of the Social Media Research Foundation
82

73.
Charting Collections of Social
Media Connections with NodeXL
Maps and reports for social media networks
April 10-12, Chicago, IL

74.
Win a Microsoft Surface Pro!
Complete an online SESSION EVALUATION
to be entered into the draw.
Draw closes April 12, 11:59pm CT
Winners will be announced on the PASS BA
Conference website and on Twitter.
Go to passbaconference.com/evals or follow the QR code link displayed on
session signage throughout the conference venue.
Your feedback is important and valuable. All feedback will be used to improve
and select sessions for future events.
84

75.
Marc@connectedaction.net
http://www.connectedaction.net
http://www.codeplex.com/nodexl
http://www.twitter.com/marc_smith
http://nodexlgraphgallery.org
http://www.slideshare.net/Marc_A_Smith
http://www.smrfoundation.org
Platinum Sponsor
Thank you!
Diamond Sponsor
April 10-12, Chicago, IL