Fbk Seminar Michela Ferron


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A brief introduction to the social network perspective and to some basic concepts in social network analysis

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Fbk Seminar Michela Ferron

  1. 1. 20/11/2008 Introduction to Social Network Analysis Michela Ferron SoNet group – Social Networking 1
  2. 2. 20/11/2008 Summary Introduction to the Social Network perspective Some basic concepts of Social Network Analysis The main structural properties in Social Network Analysis (some indices = formal measures) 2
  3. 3. 20/11/2008 The Social Networks Perspective The Social Networks Perspective Recent decades: Social network and methods of SNA interest from social and behavioral science. SNA: focus on relationships among social entities The social environment can be expressed as patterns (regularities) in relationships among interacting units Methods that are different from the traditional statistics and data analysis 3
  4. 4. 20/11/2008 Social Network Analysis  VS  VS Traditional Research Approaches pp SNA as a distinct research perspective within the social and behavioral sciences: Actors are viewed as interdependent Relational ties are channels for transfer or “flow” of resources (material and nonmaterial) Structure as a set of lasting patterns of relations among actors Focus on structure 4
  5. 5. 20/11/2008 Unit of Analysis “[…] the unit of analysis in network analysis is not the individual, but an entity consisting of a collection of individuals and the linkages among them” (Wasserman & Faust, 1994) Faust Social network analysis is focused on uncovering the patterns of people's interaction. Assumption: how an individual lives depends in large part on how that individual is tied into the larger web of social connections. 5
  6. 6. 20/11/2008 What is a Social Network?  A definition A definition “A network is a set of interconnected nodes ” (Castells, 2001, p. 1) ( , ,p ) quot;[...] A social network is a set of people (or [...] organizations or other social entities) connected by a set of social relationships, such as friendship, co-working or information exchange“ (Garton et al., 2007) 6
  7. 7. 20/11/2008 SNA Interdisciplinarity A number of different disciplines contributed to the conceptualization of SNA, among which: Formal Mathematics Statistics Computer Science Sociology (Moreno) Anthropology (Barnes) Psychology P h l 7
  8. 8. 20/11/2008 Basic example 8
  9. 9. 20/11/2008 Fields of Applications Impact of urbanization on well‐being The world politic and economic system Social support Diffusion and adoption of innovations p Cognition and social perception Community decision making Community decision making Organizational studies Epidemiology studies Epidemiology studies Studies on terrorist networks Telecommunication studies Telecommunication studies ... 9
  10. 10. 20/11/2008 Data collection Data collection Questionnaire Interview Observation Archival records Experiments ... 10
  11. 11. 20/11/2008 The concept of Relation 3 main characteristics of relations: f Content: the resource exchanged (material or  not; i.e. in CMC contexts we can talk about the  exchange of different kinds of information) Direction: Directed relation: i.e. “support relations”  giving support or receiving support Undirected relation:  i.e. “to be married to  someone”, “to be flatmates” Strength: can be operationalized in a number  g p of ways (i.e. pairs may communicate once a day,  weekly or yearly) 11
  12. 12. 20/11/2008 Network description 1. 1 Set notation 2. From the Graph Theory 3. 3 Matrix representation 12
  13. 13. 20/11/2008 Network description Examples (binary network = relations involve couples) 1. Set notation A list of all the elements of a set of actors: X = {x₁, x₂, x₃, x₄}, and a list of the pairs of elements which are linked by p y some kind of social relationship A = {(x₁, x₂), (x₂,x₁), (x₄,x₂), (x₃,x₂), (x₃,x₄), (x₄,x₃)} 13
  14. 14. 20/11/2008 Network description (2) 2. From the Graph theory Actors are represented by points (nodes or ) vertex); Relations are represented by lines (edges) between two linked points i.e. unvalued, directed graph ( di h (or di-graph): h) for every relation we can identify a receiver and a sender 14
  15. 15. 20/11/2008 Network description (3) 2. Matrix In this example: a boolean (presence/absence p (p of a relation between couples of nodes, or diads), asimmetric matrix 15
  16. 16. 20/11/2008 Why mathematics if we are talking  about social concepts? about social concepts? Linton Freeman (Research Professor of Sociology at the University of California and founder of the journal Social Networks): “There are real problems when we try to reason in ordinay language […] as problems get more complicated, they language. [ ] complicated become harder to reason through. Our thinking gets fuzzy, and it’s difficult to tell wether the informal logic we use is, in fact, logical. ” (Freeman, 1984, p. 345) Mathematics is: formal, concise, abstract, formal concise abstract unambiguous. 16
  17. 17. 20/11/2008 Main Structural Properties Nodal degree Density of a graph Centrality measures Local and global centrality (centralization) Degree centrality Betweenness centrality Closeness centrality Reciprocity 17
  18. 18. 20/11/2008 Degree Nodal Degree: number of lines incident with a node. In directed graph: Nodal indegree: number of lines directed into a node measure of RECEPTIVITY POPULARITY RECEPTIVITY, Nodal outdegree: number of lines directed from a node to another one measure of EXPANSIVENESS 18
  19. 19. 20/11/2008 Density Density of a graph: proportion of possible lines that are actually present in the graph (the ratio of the number of the present lines to the maximum possible). 19
  20. 20. 20/11/2008 Density Density: general level of linkage among the points measure of COHESION CONSTRAINT: the larger the graph (other things being equal), the lower the density. g q ), y Example: a graph of 5 actors will probably have a higher density than a graph of 5 hundred people This limitation prevents density measures being compared across networks of different sizes. f ff 20
  21. 21. 20/11/2008 Centrality The idea of centrality was one of the earliest in SNA. Centrality is one of the most studied proliferation of formal measures, and thus sometimes, confusion. Freeman (1979) talks of both: “point centrality” point centrality relative prominence of points and “graph centrality” graph centrality overall cohesion or integration of the graph 21
  22. 22. 20/11/2008 Local centrality based on nodal  degree Nodal degree: a measure of centrality (it shows how well connected the point are within their local environment) BUT: nodal degree depends on the group size constraints for comparisons p Degree centrality g y An actor has a high degree centrality if he/she is very active has many ties to other actors. Prominence = “activity” or “degree” 22
  23. 23. 20/11/2008 Local centrality based on  betweenness Betweenness centrality: Interactions between two nonadjacent actors might depend on other actors, who might have some control over the interactions of the others. An actor has a high betweenness centrality if he/she lies between many of other actors (technically, on their geodesic) Prominence = “control on communication” 23
  24. 24. 20/11/2008 Local centrality of a node (3) Closeness centrality: focuses on how close an actor is to all the others in the network. An actor has a high closeness centrality if he/she can quickly interact with all others. q y In a communication context, he/she doesn’t need , to rely on other actors for the relaying of information (short communication paths to the others) Prominence = “independence” or “efficiency” 24
  25. 25. 20/11/2008 Global centrality or centralization For every measure of local centrality there is a corresponding measure of global centrality, or “centralization”: These measures quantify the variability (dispersion, range) of the individual actor indices. In general, Degree, Betweenness, and Closeness centralization grow as the network become less homogeneous and thus more centralized i.e. they are maximum in the sociometric star 25
  26. 26. 20/11/2008 Reciprocity Fundamental question: how strong is the tendency for one actor to choose another one, if the second actor chooses the first? Reciprocity is an index of mutuality, it shows the p y y tendency to reciprocate choices more frequently than by chance. It’s more that a descriptive measure: it’s based on the expectation of the number of mutual dyads. 26
  27. 27. 20/11/2008 Thank you. y … Questions? 27
  28. 28. 20/11/2008 References and Resources Castells, M. (2001). The Internet Galaxy. New York: Oxford University Press Inc. Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks 1 215-239 clarification Networks, 1, 215-239. Freeman, L. C. (1984). Turning a profit from mathematics: The case of social networks. Journal of Mathematical Sociology, 10, 343-360. Garton, L., Haythornthwaite, C., & Wellman, B. (1997). Studying online social networks. Journal of Computer networks Computer- Mediated Communication, 3(1). Retrieved November, 7th, 2008 from http://jcmc.indiana.edu/vol3/issue1/garton.html. 28
  29. 29. 20/11/2008 References and Resources (2) Katz, L., & Powell, J. H. (1955). Measurement of the tendency toward reciprocation of choice. Sociometry, 18(4), 403-409. Wasserman, S., & Faust, K. (1994). Social network analysis. Methods and applications. Cambridge, MA: C b id U i Cambridge University P it Press. Wellman, B. (1997). An electronic group is virtually a social network. In S. Kiesler (Ed.), Culture of the Internet (pp. 179- ( ), (pp 205). Mahwah, NJ: Lawrence Erlbaum Associates. 29