Tools for Social Network Analysis & Visualisation

suresh.sood@uts.edu.au

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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