4. Why ?
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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
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Explore all sorts of data including combination of unstructured & structured
5. 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
6. How ?
Train of Thought Analysis
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•
•
•
•
•
•
•
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
7. Social Network Representation
• Primary focus is actors & relationships # actors & attributes
• Nodes (Actors) connected by Links (Ties/relationship or edge)
Adjacency list
• Links represent flows or transfer
– material goods or information
2
1
Graph or
sociogram
3
Adjacency matrix
Actors
1
1 0
2 1
3 0
2
1
0
1
3
0
1
0
Relationship
1 = presence of link
0 = no direct link
1: 2
2: 1, 3
3: 2
8. 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
location
latitude
longitude
street-address
locality
region
postal-code
country-name
Contact Info :
email
phone_number
fax_number
8
11. 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
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Reduced the suspect list from 18 million to 230
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Further analysis with the use of additional information reduced this to 32
15. 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
16. 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)
18. Standard Data Sets
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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
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FREEMAN'S EIES DATA
GAGNON & MACRAE PRISON
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GALASKIEWICZ'S CEO'S AND CLUBS
KAPFERER MINE
KAPFERER TAILOR SHOP
KNOKE BUREAUCRACIES 10 organizations and two relationships – money & info exchange
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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
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Borgatti, S.P., Everett, M.G. and Freeman, L.C. 2002. Ucinet 6 for Windows. Harvard: Analytic Technologies.
19. NodeXL - Excel 2007/10/13 workbook template for viewing and analyzing network graphs
http://nodexl.codeplex.com/releases/view/108288
20. Import ego, Fan page and groups networks from Facebook using
Social Network Importer for NodeXL
http://socialnetimporter.codeplex.com/
Social graph in the following order: you, your social network friends, friends-of-friends, your followers, and the overall community.Wall Street feed – simple way to navigate social network of friends social gestures and your –efficient, increased engagement , increases importance of attention info c.f. banking – remember fuss around news feedGoogle Open Social Attention Streams (already included in Plaxo Pulse) - MySpace Friends Updates -Netvibes Activities-LinkedIn Network UpdatesHigh social engagement vs traditional media (radio, tv, print, outdoor) with low engagement. This is about dialogue, interactivity, informality, people + technology & niche NOT Tradigital for mass using push, automation & technology only. Social Media Marketing practice centres around – networks, communities, blogs and microblogging. Traditional business functions can be socialised e.g. legal, supply chain, R&D, HR…Social Strategy (Media) - through sharing; engaging; building relationships and influencingincrease our reach, influence and relevancecreate ambassadors to support and promote what we dopersonalise interactionsencourage and grow communities through a critical mass of active cultural and scientific participants maximise revenuechange our work models from one-to-one communication to many-to-many communicationmove from providing information to creating shared meaning with audiences
Diana – max links (degree centrality) most connected – connector or hub – number of nodes connected – high influence of spreading info or virusHeather – best location powerful figure as broker to determine what flows and doesn’t –single point of failure – high betweeness = high influence – position of node as gatekeeper to exploit structural holes (gaps in network)Fernado & Garth – shortest paths = closeness – the bigger the number the less centralEigenvector = importance of node in network ~ page rank google is similar measure – being connected to well connected a popularity and power measure
