Workshop B - Tools for SNA
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Transcript of Workshop B - Tools for SNA
Tools for Social Network Analysis & Visualisation
Geektoid Mangala
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Suresh S.
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Agenda
1. Why?
2. Social Network Representation
3. Tools and Visualisations
Why ?• New insights from social network data
- patterns of activity & trends not previously known can be identified
- power of the human mind is harnessed to uncover patterns of human interaction:
Outliers e.g. isolated individuals Ego centric networks Cliques Network cutpoints Boundary riders
• Explore all sorts of data including combination of unstructured & structured
How Social Network Analysis Helps Educators
• learner isolation (McDonald, Stuckey, Noakes, & Nyrop, 2005)
• creativity (Burt, 2004; McWilliam & Dawson, 2009)
• community formation (Dawson, 2008; Lally, Lipponen, & Simons, 2007)
• Group cohesion education evaluative tool Reffay and Chanier (2002)
• Social interactions in growing classes (Brooks, et al, 2009)
• Social relationships between learners (Brooks, et al, 2009)
Taken from SNAPP: Realising the affordances of real-time SNA within networked learning environments, Networked Learning Conference 2010
How ?Train of Thought Analysis
• A bottom-up approach • Perceptual process of discovery to uncover structure• Distinguish patterns,structure, relationships and anomalies• Reveals indirect links • Knowledge is colour coded• Marketing Analyst can spot irregularities• Not sure why but where does this lead• Harnesses the power of the human mind
Data Information Knowledge
Social Network Representation
• Primary focus is actors & relationships # actors & attributes
• Nodes (Actors) connected by Links (Ties/relationship or edge)
• Links represent flows or transfer– material goods or information
1 2 30 1 01 0 10 1 0
123
1: 22: 1, 33: 2
1
32
Adjacency matrix
Adjacency list
1 = presence of link0 = no direct link
Actors Relationship
Graph orsociogram
Facebook Object Types for Social Graph
Activities Businesses Groups Organizations People Places Products and Entertainment
Activity Bar Cause Band Actor City Album
Sport Company Sports_league Government Athlete Country Book
Cafe Sports_team Non_profit Director Landmark Drink
Hotel School Musician State_province Food
Restaurant University Politician Game
Public_figure Product
Song
Movie
Tv_show
Websites UPC/ISBN Other
Blog UPC code Other
Website ISBN number
Article
latitude longitude street-addresslocality regionpostal-codecountry-name
locationContact Info : emailphone_numberfax_number
8
9
10
How to Find a Killer using Visualisation
• 1990’s Ivan Milat killed 7 backpackers making him Australia's most notorious Serial Killer
• Everyone in Australia was a suspect
• Enormous volumes of data from multiple sources
RTA Vehicle records Gym Memberships Gun Licensing records Internal Police records
• • Police applied visualisation techniques (NetMap) to the data
• Reduced the suspect list from 18 million to 230
• Further analysis with the use of additional information reduced this to 32
Visualising Popular Social Networks
• Facebook– vansande.org/facebook/visualiser/– www.touchgraph.com/facebook
• Facebook (data extraction)– apps.facebook.com/netvizz– apps.facebook.com/namegenweb/– apps.facebook.com/myfnetwork/
• LinkedIn– inmaps.linkedinlabs.com/network
• LinkedIn + Facebook
• Twitter– mentionmapp.com
YouTube Insight – Video Analytics
Key Network Measures
• Degree Centrality• Betweenness Centrality• Closeness Centrality• Eigenvector Centrality
krackkite.##h (modified labels)
Connector(hub)
Diana’sClique
Broker
Boundary spanners
Contractor ? Vendor
UCINET 6
• UCINET IV for DOS is free
• Grab bag of techniques and procedures
• Matrix centered view – rows & columns - actors– cell value - relationship
• Citation – Borgatti, S.P., M.G. Everett, and L.C. Freeman. 1999. UCINET 6.0 Version 1.00.
Natick: Analytic Technologies.
• Network analysis requires:– ##h file contains meta data about the network – ##d file contains the actual data about the network
Useful References
• Tutorial Prof Hanneman (http://faculty.ucr.edu/~hanneman/nettext/)
• Network Analysis in Marketing (Webster & Morrison 2004)
• www.insna.org (international network for social analysis)
Data Language (DL) Filetype
dl n=4 format=fullmatrix data: 0 1 1 0 1 0 1 1 1 1 0 0 0 1 0 0
dl n=4 labels: Sanders,Skvoretz,S.Smith,T.Smith data: 0 1 1 0 1 0 1 1 1 1 0 0 0 1 0 0
dl nr = 6, nc = 4
col labels:
hook,canyon,silence,rosencrantz
data:
0 1 1 0
1 0 1 1
1 1 0 0
dl nr = 6, nc = 4row labels embedded
col labels embeddeddata:
Dian Norm Coach SamMon 0 1 1 0Tue 1 0 1 1Wed 1 1 0 0Thu 0 1 0 0Fri 1 0 1 1 Sat 1 1 0 0
Standard Data Sets• BERNARD & KILLWORTH
– FRATERNITY interactions among students living in a fraternity at a West Virginia college– HAM RADIO radio calls made over a one-month period (voice-activated recording device)– OFFICE interactions in a small business office. – TECHNICAL
• CAMP 92• COUNTRIES TRADE DATA• DAVIS SOUTHERN CLUB WOMEN observed attendance at women’s club in 1930s
• FREEMAN'S EIES DATA• GAGNON & MACRAE PRISON
• GALASKIEWICZ'S CEO'S AND CLUBS• KAPFERER MINE• KAPFERER TAILOR SHOP• KNOKE BUREAUCRACIES 10 organizations and two relationships – money & info exchange
• KRACKHARDT HIGH-TECH MANAGERS• KRACKHARDT OFFICE CSS• NEWCOMB FRATERNITY• PADGETT FLORENTINE FAMILIES• READ HIGHLAND TRIBES• ROETHLISBERGER & DICKSON BANK WIRING ROOM• SAMPSON MONASTERY Experimental and case study of social relationships." Doctoral dissertation, Cornell
Univ.• SCHWIMMER TARO EXCHANGE• STOKMAN-ZIEGLER CORPORATE INTERLOCKS• THURMAN OFFICE• WOLFE PRIMATES• ZACHARY KARATE CLUB
• Borgatti, S.P., Everett, M.G. and Freeman, L.C. 2002. Ucinet 6 for Windows. Harvard: Analytic Technologies.
NodeXL - Excel 2007/10/13 workbook template for viewing and analyzing network graphs
http://nodexl.codeplex.com/releases/view/108288
Import ego, Fan page and groups networks from Facebook using Social Network Importer for NodeXL
http://socialnetimporter.codeplex.com/
Caution!
“Children never put off till tomorrow what will keep them from going to bed tonight”
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