Ces social network analysis presentation laura garton


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Social Network Analsysis: Challenges and Opportunities for Evaluation Think Tank Session

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  • Why study social networks?Social networks support extensive, complex and dynamic systems of exchange, influence and interaction. They affect life chances and outcomes because informal relationships link individuals not only to others in their immediate social realm, but they reach beyond this via the contacts of friends and acquaintances. Connected: the amazing power of social networks and how they shape our lives. London: HarperPress. (Christakis and Fowler, 2010)Social units can refer to a range of entities at multiple levels of analysis, such as students in a classroom, businesses in an industry, countries in an economic market, or members in a gang
  • unit of analysis is not the individual but a collection of individuals (or entities) and the linkages among themUses the perspective of a social system and examines the regular patterns among the parts of the systemExample of corporate giving to charities – typical approach would be to sample and survey corporations on their giving patterns, sna would look at social influence factors and things like memberships on each other’s board, joint business dealings, Example of Granovetters strength of weak ties. Race becomes important variable only as it pertains to particular kinds of patterns in networks – Collins 1988 Can also just look at the pattern of relations without individual data – e.g. trading relations as exhibiting core-periphery structure –Also can look at changes in network over time e.g. trading patterns, collaborationBarry Wellman refers to the network concept that social structure is best understood as a set of relations and positions and that social categories could best be understood and discovered by examining the relations between and among social actors or institutionsExamples – contagion, diffusion of innovation, strength of weak ties, social support, social capital, cultural capital, power,
  • Degree Centrality: The number of direct connections a node has. What really matters is where those connections lead to and how they connect the otherwise unconnected. 2. Betweenness Centrality: A node with high betweenness has great influence over what flows in the network indicating important links and single point of failure. 3. Closeness Centrality:. They have the shortest paths toall others. ) – use of graph theory, statistical and probability theory and algebraic models – probability distributions for relational data – mathematical models for relational networks – UCINET (Steve Borgatti) – visualization of data using KRACKPLOTMany of the current formal concepts in SNA such as span, connectedness, clusterability and multiplexity were introduced in the 1950’s and 60’s as a way to describe properites of social structures and individual social environments. Through small group studies of group problem solving and individual performance in 1950’s by Leavitt, Bravelas and others developed the notions of star centrality and group centralization Findings on tendencies toward reciprocity, mutality of positive affect, structural balance and transitivity were discovered early and had a profound effect on the study of social structure. - Theory testing – example testing the supposition that among families one would expect to find more relations of reciprocity of support or exchange of materials.
  • Leadership Learning Community developed a series of maps to see where there were relevant conversations happening in Twitter and how those conversations were connected.  This is a map of the clusters generated by keyword searches relevant to urban health.In analyzing the map we see that different conversations have different shapes. ‘Complete streets’, ‘walkability’ and ‘smart growth’ are distinct conversations, but they are highly interconnected in this cluster. There are a significant number of yellow squares, which represent Twitter users that are using two or more keywords in their tweets. They are actually weaving together these three keywords or hashtags.  Some examples include: @Transportdata, which is an organization that is focused on transportation policy and is bridging between ‘complete streets’ and ‘smart growth’; and another example is @jeromeoppenheim, who is a free agent covering urban policy issues and bridging between ‘complete streets’, ‘smart growth’ and ‘RWJF’. By contrast, ‘saveplay’ is a highly interconnected conversation with people retweeting and responding to each other, but with many fewer bridgers to other clusters (only two). And ‘healthy food access’ has a very different shape—many more dispersed nodes. This is less a conversation, and more a group broadcasting messages. RWJF seems to be driving this group, along with @naomistarkman (from civileats.com).  Also, @Healthyamerica1 has a unique position in this map– the account is bridging between the ‘healthy food access’ and ‘complete streets’ conversations.
  • It isn’t clear from these two visuals what benefits the “Connecting the Dots” process had on the group over time. These types of visuals can be misleading if there isn’t more contextual information. Krebs (2002) notes that network analysis can help a group explore options for adapting to a changing environment. Connections, strengths, and weaknesses are made visible, helping answer many key questions in the collaboration community-building process:Are the right connections in place? Are any key connections missing?Who are the people playing leadership roles in the community?Who are not, but could be?Who are the experts in process, planning and practice?Who are the mentors others seek out for advice?Who are the innovators? Are ideas shared and acted upon?(Source:Valdis Krebs and June Holley, “Building Smart Communities through Network Weaving,” 2002)
  • Ces social network analysis presentation laura garton

    1. 1. Laura Gartonlaura@garton.caCES Conference 2013Think Tank Session
    2. 2.  Introductions and check-in on level ofknowledge/use of SNA by participants Introduction of key concepts of SNA andexamples of use in social research Table discussion of potential for using SNA inevaluation Table discussion of challenges experienced orforeseen in using SNA How CES could support practitioners
    3. 3.  It is not social media (but may use socialmedia data) Nor is it networking It is not a graph or network map (althoughanalysis may produce one) It is not a marketing tool (although may beused by marketing)
    4. 4. Social network analysis is based on an assumption of the importance ofrelationships between actors and that these networks of relations supportcomplex and dynamic systems of exchange, influence and interaction.The unit of analysis in network analysis is not the individual, but an entityconsisting of a collection of individuals and the linkages among them.Network methods focus on dyads (two actors and their ties), triads (threeactors and their ties), or larger systems (subgroups of individuals), orentire networks).Wasserman, S. and K. Faust, 1994, Social Network Analysis. Cambridge: Cambridge UniversityPressSNA is a Perspective & a Methodology
    5. 5.  Links micro to macro Through ties flow resources Actors are embedded in network of relations --social, economic, and political structures View social categories as patterns of relations -gender, race, SES Contagion theories, diffusion of innovation, socialcapital, social influence, strength of weak ties
    6. 6.  Centrality◦ Degree◦ Betweeness◦ Closeness◦ Centralization Density Reciprocity HomophilyKrackhardt Kite
    7. 7.  Employment Health Counter terrorism Crime Leadership Social movements Community Retail
    8. 8. Leadership Learning Community in partnership with RWJFhttp://leadershiplearning.org/blog/nataliaca/2012-01-30/applying-social-network-analysis-online-communications-networksHow to increase reach and influence in social media space
    9. 9. Krebs, V. & Holley, J. (2002). Building Smart Communities through Network Weaving
    10. 10.  Systems – multi level perspective Inform intervention design Help articulate theory of change Reveal hidden or underlying relationships
    11. 11.  Generating network members and dealingwith missing data Questions used to define relations andoperationalize concepts Sensitivity when revealing individual leveldata about relations between networkmembers Complex and contested statistical procedures Descriptive vs explanatory Interpretation issues Other?