DREaM Event 2: Louise Cooke


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Slides to accompany Dr Louise Cooke's workshop session "An introduction to social network analysis" presented at DREaM Event 2.

For more information about the event, please visit http://lisresearch.org/dream-project/dream-event-2-workshop-tuesday-25-october-2011/

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  • Introduce terms nodes, actors, dyadic, ties. Also, discuss directional and valued ties.
  • Note that the term ‘social’ is used in its broadest conception to imply a relationship – it does not necessarily imply a relationship of a ‘social’ kind. SNA does not just apply to the analysis of social networking sites! Based on mathematical graph theory.
  • Sets of nodes, linked by ties. Mention also the Katrina ‘blame game’.
  • Connectors = Germany and Finland. Mavens = Slovenia, Finland, then Russia. (Where people go to in network for knowledge).
  • Six degrees of Kevin Bacon – any film actor can be linked by no more than 6 ties to Kevin Bacon. Small world phenomenon of 6 degrees of separation – everyone is an average of 6 “steps” away from each person on earth.
  • Euler – Swiss mathematician and physicist, made significant developments in graph theory and mathematical notation. Hawthorne studies – bank wiring room experiments revealed existence of informal groups or ‘cliques’ within formal groups. Workers were more responsive to peer pressure of social group than incentives offered by management. Hence it is in organisations’ interest to collaborate with informal groups. 429 papers published 2010-11. What happened in mid-1990s? Take-up of W3 with introduction of Mosaic browser in 1993.
  • Strength of weak ties – Granovetter Embeddedness – work related transactions tend to overlap with patterns of social relations (Granovetter, 1985). Business is embedded in social networks: knowledge transfer networks tend to emulate social networks. Social capital – lack of connectedness can impact negatively on business success.
  • We are taking the concepts of nodes, ties (also called arcs and edges) and dyads as already understood. Component = subset of graph where there is a path between each other. Total connectivity = all nodes in network are one component. Density = indicator of general level of connectedness. If every node is directly connected to every other = complete graph. Degree centrality of a node = no. of ties to node. Closeness centrality of a node = total distance of this node from all other nodes. Betweenness centrality = number of times a node needs a given node to reach another node. Cliques – subgraph in which any node is directly connedted to any other node of the subgraph.
  • DREaM Event 2: Louise Cooke

    1. 1. An Introduction to Social Network Analysis DREaM Workshop October 2011 Louise Cooke Senior Lecturer
    2. 2. This Session <ul><li>An introduction to Social Network Analysis (SNA) </li></ul><ul><li>Data collection, analysis and visualisation </li></ul><ul><li>Example of SNA project </li></ul><ul><li>Complete network surveys </li></ul><ul><li>This afternoon we will review the survey results. </li></ul>
    3. 3. Learning objectives <ul><li>To understand the meaning and potential uses of Social Network Analysis (SNA) </li></ul><ul><li>To introduce some core social network concepts and theories </li></ul><ul><li>To carry out a practical exercise analysing our own networks </li></ul><ul><li>To identify when and for what purposes SNA might be used in information science research. </li></ul>
    4. 4. Fact finding <ul><li>Has anyone used SNA in their research? </li></ul><ul><li>Or plans to do so? </li></ul><ul><li>What do you understand by SNA? </li></ul>
    5. 5. Some Network Definitions (OED) <ul><li>Work in which threads, wires or similar materials are arranged in the fashion of a net; </li></ul><ul><li>A complex collection or system of rivers, canals, railways or the like; </li></ul><ul><li>An interconnected chain or system of immaterial things (e.g. events); </li></ul><ul><li>A system of cables for the distribution of electricity; </li></ul><ul><li>A nation-wide broadcasting company; </li></ul><ul><li>An inter-connected group of people; </li></ul><ul><li>A series of linked computers to make possible functions such as the transfer of data or the sharing of processing capabilities. </li></ul>
    6. 6. What is a Network? <ul><li>A set of dyadic ties all of the same type, among a set of actors (or nodes ) </li></ul>
    7. 7. For example…
    8. 8. What Is Social Network Analysis? <ul><li>What is unique about SNA is that it focuses on relationships between entities rather than properties of entities. </li></ul><ul><li>There is an underlying perspective that “Structure Matters” and that many social phenomena can be better understood by taking dyadic and structural data into consideration. </li></ul>
    9. 9. An example of a network diagram
    10. 10. Knowledge Exchange among EU Countries Source: www. Orgnet.com Based on data from 2003-4.
    11. 11. A Growing Area of Interest <ul><li>Popular culture </li></ul><ul><ul><li>Kevin Bacon </li></ul></ul><ul><ul><li>Online social networking </li></ul></ul><ul><li>Business practitioners </li></ul><ul><ul><li>New consulting tools </li></ul></ul><ul><ul><li>Knowledge management </li></ul></ul><ul><li>Academic study </li></ul><ul><ul><li>Multiple fields e.g. communication, epidemiology. </li></ul></ul>
    12. 12. History of Social Network Analysis <ul><li>1736- Euler </li></ul><ul><li>1930s- Hawthorne studies </li></ul><ul><li>1940s Psychologists </li></ul><ul><li>1950s & 60s Anthropologists </li></ul><ul><li>1970s Rise of Sociologists </li></ul><ul><ul><li>Small Worlds, Strength of weak ties </li></ul></ul><ul><li>1980s Growth in computation </li></ul><ul><li>1990s Ideas spread </li></ul><ul><ul><li>UCINET released, spread of network analysis to multiple fields, social capital, embedded ties </li></ul></ul><ul><li>2000s Social networking as populist phenomenon. </li></ul>Papers retrieved from Google Scholar search using “Social Network Analysis” in title, September 2011
    13. 13. Key Social Network Theories <ul><li>Small World Phenomenon </li></ul><ul><li>Strength of Weak Ties </li></ul><ul><li>Embeddedness </li></ul><ul><li>Social Capital. </li></ul>
    14. 14. Relations Among Persons <ul><li>Kinship </li></ul><ul><ul><li>Father of, mother of </li></ul></ul><ul><li>Other </li></ul><ul><ul><li>Boss of, teacher of </li></ul></ul><ul><ul><li>Friend of </li></ul></ul><ul><li>Cognitive/Perceptive </li></ul><ul><ul><li>Knows </li></ul></ul><ul><ul><li>Aware of what they know </li></ul></ul><ul><li>Affective </li></ul><ul><ul><li>Likes, trusts </li></ul></ul><ul><li>Interactions </li></ul><ul><ul><li>Gives advice to, fights with </li></ul></ul><ul><ul><li>Cites </li></ul></ul><ul><ul><li>Has sex with </li></ul></ul><ul><li>Affiliations </li></ul><ul><ul><li>Belongs to same clubs </li></ul></ul>
    15. 15. Relations Among Organisations <ul><li>As corporate entities </li></ul><ul><ul><li>Refer to, Buy from, sell to, leases too, outsources to </li></ul></ul><ul><ul><li>Owns shares of, subsidiary of </li></ul></ul><ul><ul><li>Joint ventures, alliances </li></ul></ul><ul><ul><li>Regulates. </li></ul></ul><ul><li>Via their members </li></ul><ul><ul><li>Personnel flows </li></ul></ul><ul><ul><li>Interlocking directorates </li></ul></ul><ul><ul><li>Personal friendships </li></ul></ul><ul><ul><li>Co-memberships. </li></ul></ul>
    16. 16. Node Types <ul><li>Persons </li></ul><ul><li>Organizations </li></ul><ul><li>Countries </li></ul><ul><li>Animals </li></ul><ul><li>Words </li></ul><ul><li>Web pages </li></ul><ul><li>Families </li></ul><ul><li>Etc. </li></ul>
    17. 17. Core concepts in SNA <ul><li>Directed and undirected networks (for example, compare co-authorship with citation) </li></ul><ul><li>Paths, components and total connectivity </li></ul><ul><li>Density and centrality </li></ul><ul><ul><li>Degree centrality </li></ul></ul><ul><ul><li>Closeness centrality </li></ul></ul><ul><ul><li>Betweenness centrality </li></ul></ul><ul><li>Cliques. </li></ul>
    18. 18. SNA and Knowledge Management <ul><li>Differences between formal and informal structures </li></ul><ul><li>Post-merger integration </li></ul><ul><li>Impact on information and knowledge flows </li></ul><ul><li>Identification of bottlenecks, information brokers, boundary spanners, sub-groups, peripherals </li></ul><ul><li>Recognising Communities of Practice. </li></ul>
    19. 19. Other Uses in Information Science <ul><li>Citation analysis </li></ul><ul><li>Internet data mining </li></ul><ul><li>Technology diffusion and adoption </li></ul><ul><li>Any others? </li></ul>
    20. 20. Other Applications <ul><li>Epidemiology </li></ul><ul><li>Viral Marketing </li></ul><ul><li>Internet data mining </li></ul><ul><li>Diffusion of innovation and new ideas e.g. political unrest </li></ul><ul><li>Conflict Management </li></ul><ul><li>Anti-terrorism and law Enforcement. </li></ul>
    21. 21. Data Collection <ul><li>Questionnaire survey (or interview) </li></ul><ul><li>Observations </li></ul><ul><li>Using existing datasets, e.g. email transactions, trade statistics, citation analysis etc. </li></ul>
    22. 22. Software Tools for Analysis and Visualisation <ul><li>UCINET </li></ul><ul><ul><li>Allows for the computational aspects of analysis, including calculating various measures (e.g., centrality, cohesion, brokerage) among others, as well as hypothesis testing </li></ul></ul><ul><li>NetDraw </li></ul><ul><ul><li>Allows for graphic representation of networks including relations and attributes </li></ul></ul><ul><ul><li>Has some analytic capabilities that partially overlap with UCINET </li></ul></ul><ul><li>There are others, generally for particular niches </li></ul><ul><ul><li>Pajek (Better at computational analysis of really large networks) </li></ul></ul><ul><ul><li>E-Net (analyzing ego networks) </li></ul></ul><ul><ul><li>KeyPlayer (influencing or disrupting networks). </li></ul></ul>
    23. 23. A Practical Example: Research Networks - by University (1)
    24. 24. Research Networks – by University (2)
    25. 25. Case studies <ul><li>Some other examples of the use of SNA </li></ul>
    26. 26. Some Ethical Issues <ul><li>Data cannot usually be collected anonymously, but it can be presented anonymously </li></ul><ul><li>Relationship ties can be a sensitive issue! </li></ul><ul><li>Accurate interpretation of the meaning behind the data is key. </li></ul>
    27. 27. Now please complete your questionnaire <ul><li>Demographic data </li></ul><ul><li>Knowledge and expertise </li></ul><ul><li>Acquaintanceship ties </li></ul><ul><li>Research ties </li></ul>
    28. 28. References & useful sources of further information <ul><li>www.analytictech.com (UCINET software) </li></ul><ul><li>Cheuk, B (2007) SNA: Its application to facilitate knowledge transfer. Business Information Review, 24 (3) 170-176. </li></ul><ul><li>Cross, R & Parker, A (2004) The Hidden Power of Social Networks: How work really gets done in organizations. Boston, Ma., Harvard University Press. </li></ul><ul><li>DeJordy, R. (2006) Social Network Analysis . Research Methods Summer School, University of Essex, July 2006. </li></ul><ul><li>Hanneman, R.A. & Riddle, M. Introduction to Social Network Methods. Free internet resource based on Ucinet at http://www.faculty.ucr.edu/~hanneman/nettext/ </li></ul><ul><li>Johnson, B & Oppenheim, C (2007) How socially connected are citers to those that they cite? Journal of Documentation , 63 (5) 609-637. </li></ul><ul><li>Kilduff, M & Tsai, W (2003) Social Networks and Organizations. London, Sage. </li></ul><ul><li>Orgnet.com Social Network Analysis software & services for organizations, communities, and their consultants. http://www.orgnet.com/ </li></ul><ul><li>Otte, E & Rousseau, R (2002) SNA: a powerful strategy, also for the information sciences. Journal of Information Science , 28 (6) 441-453. </li></ul>
    29. 29. Summary of important points <ul><li>SNA is used to analyse the relationship patterns between entities </li></ul><ul><li>It can be used to analyse networks of very different kinds (and not just those that we understand as ‘social’) </li></ul><ul><li>It is based on mathematical sociology and graph theory </li></ul><ul><li>In organisations, it can help us to diagnose and understand information and knowledge flows and bottlenecks. </li></ul>
    30. 30. To follow… <ul><li>Results of our survey </li></ul>
    31. 31. Any Questions?