Workshop B - Tools for SNA

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  • 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

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  • 1. Tools for Social Network Analysis & Visualisation GreatMystery14 Suresh S. soody soody www.facebook.com/sureshsood ssood Hero5! twitter.com/soody www.linkedin.com/in/sureshsood Geektoid Mangala google.com/+sureshsood suresh.sood@uts.edu.au http://bit.ly/1dIb52c scuzzy55
  • 2. Agenda 1. Why? 2. Social Network Representation 3. Tools and Visualisations
  • 3. 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
  • 4. 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
  • 5. 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
  • 6. 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
  • 7. 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
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  • 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
  • 11. 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
  • 12. YouTube Insight – Video Analytics
  • 13. Key Network Measures krackkite.##h (modified labels) • • • • Diana’s Clique Degree Centrality Betweenness Centrality Closeness Centrality Eigenvector Centrality Connector (hub) Vendor Contractor ? Broker Boundary spanners
  • 14. 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
  • 15. 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)
  • 16. 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 row labels embedded col labels embedded data: Dian Norm Coach Sam Mon 0 1 1 0 Tue 1 0 1 1 Wed 1 1 0 0 Thu 0 1 0 0 Fri 1 0 1 1 Sat 1 1 0 0
  • 17. 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.
  • 18. NodeXL - Excel 2007/10/13 workbook template for viewing and analyzing network graphs http://nodexl.codeplex.com/releases/view/108288
  • 19. Import ego, Fan page and groups networks from Facebook using Social Network Importer for NodeXL http://socialnetimporter.codeplex.com/
  • 20. Caution! “Children never put off till tomorrow what will keep them from going to bed tonight” ADVERTISING AGE