SN- Lecture 9

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SN- Lecture 9

  1. 1. Social Capital & Network Characteristics Lecture 9
  2. 2. Aims Lecture 9 To understand: To study characteristics of networks Networks as social capital The problem of only a structuralist approach+ + + Homophily Reciprocity Centrality
  3. 3. Some Questions Social network research requires general theories to answer: In Network Research
  4. 4. Some Questions Social network research requires general theories to answer: Can the effects of networks (i.e., on behavior) be generalized across situations? In Network Research a.
  5. 5. Some Questions Social network research requires general theories to answer: Can the effects of networks (i.e., on behavior) be generalized across situations? In Network Research a. Why certain network effects sometimes occur and sometimes not?b. and if not, i.e., Why is there more clustering in some networks than in others?
  6. 6. Structure & Social Capital
  7. 7. Structuralism Structure overrides preferences A first approach You can explain people’s actions by only knowing the structure of their social network Claims: + +
  8. 8. Structuralism Structure overrides preferences A first approach You can explain people’s actions by only knowing the structure of their social network Claims: + + Give me the network & I will tell you what the actors will do
  9. 9. Selling Point Labor markets (Granovetter, 1974) Of this perspective Illegal services: Abortion (Lee, 1969) All markets: + + Are socially organized in networks
  10. 10. Selling Point Labor markets (Granovetter, 1974) Of this perspective Illegal services: Abortion (Lee, 1969) All markets: + + Are socially organized in networks Role equivalence: Persons are tied not to the same persons but to similar persons (Wasserman & Faust, 1994)
  11. 11. Selling Point Labor markets (Granovetter, 1974) Of this perspective Illegal services: Abortion (Lee, 1969) All markets: + + Are socially organized in networks Role equivalence: Persons are tied not to the same persons but to similar persons (Wasserman & Faust, 1994) Two positions in the aggregate: an elite person (well-connected) & a hanger-on (not well-connected)
  12. 12. Main Problems Of structuralism It lacks a theory of individual behavior
  13. 13. Main Problems Think about the micro-macro link Of structuralism It lacks a theory of individual behavior +
  14. 14. Main Problems Think about the micro-macro link Of structuralism It lacks a theory of individual behavior + Rational Choice Perspective: Conceives networks as social resources
  15. 15. Main Problems Think about the micro-macro link Of structuralism It lacks a theory of individual behavior + Rational Choice Perspective: Personal networks can be treated as social capital that is instrumental in reaching our goals Conceives networks as social resources
  16. 16. Not a new idea Since Hobbe’s Leviathan:
  17. 17. Not a new idea Thomas Hobbes English philosopher 1588-1679 To have friends is to have power: for they are strengths united Since Hobbe’s Leviathan:
  18. 18. Networks as Social Capital Networks are treated as a specific resource important for most goals people have in life.
  19. 19. Networks as Social Capital Two main propositions in S.C. Theory Networks are treated as a specific resource important for most goals people have in life.
  20. 20. Networks as Social Capital Two main propositions in S.C. Theory Networks are treated as a specific resource important for most goals people have in life. 1 Social Resource Hypothesis: people better equipped with social capital will be better able to attain their goals
  21. 21. Networks as Social Capital Two main propositions in S.C. Theory Networks are treated as a specific resource important for most goals people have in life. 1 Social Resource Hypothesis: people better equipped with social capital will be better able to attain their goals 2 Investment Hypothesis: people will invest in social capital according to its instrumental value in producing their ends
  22. 22. Networks as S.C. It explains the emergence as well as the effects of social networks
  23. 23. Networks as S.C. It explains the emergence as well as the effects of social networks A person’s social capital promotes her goal achievement
  24. 24. Networks as S.C. It explains the emergence as well as the effects of social networks She will invest in it depending on its instrumental value & A person’s social capital promotes her goal achievement
  25. 25. Networks as S.C. It explains the emergence as well as the effects of social networks Macro-micro link She will invest in it depending on its instrumental value & A person’s social capital promotes her goal achievement
  26. 26. Networks as S.C. It explains the emergence as well as the effects of social networks Macro-micro link She will invest in it depending on its instrumental value Micro-macro link & A person’s social capital promotes her goal achievement
  27. 27. Homophily
  28. 28. Practical 11 Choose links & Actions
  29. 29. Homophily If instead of just looking at the network We keep track of characteristics of the nodes (i.e., attributes) Lazarsfeld & Merton (1954)
  30. 30. Homophily If instead of just looking at the network We keep track of characteristics of the nodes (i.e., attributes) We tend to find that link nodes are similar to each other Lazarsfeld & Merton (1954)
  31. 31. Homophily If instead of just looking at the network We keep track of characteristics of the nodes (i.e., attributes) We tend to find that link nodes are similar to each other Birds of a feather flock (will fly) together Philemon Holland, 1960 Lazarsfeld & Merton (1954)
  32. 32. Real-life networks Homophily Race & friendship networks US: Interracial marriages US: Gender & friendship networks
  33. 33. Real-life networks Homophily Only 8% of people have any people of another race that they discuss important matters with (Marsden, 1987) Race & friendship networks US: Interracial marriages US: Gender & friendship networks
  34. 34. Real-life networks Homophily Only 8% of people have any people of another race that they discuss important matters with (Marsden, 1987) Race & friendship networks US: 1% of white marriages, 5% of black marriages, 14% of asian marriages (Fryer, 2006) Interracial marriages US: Gender & friendship networks
  35. 35. Real-life networks Homophily Only 8% of people have any people of another race that they discuss important matters with (Marsden, 1987) Race & friendship networks US: 1% of white marriages, 5% of black marriages, 14% of asian marriages (Fryer, 2006) Interracial marriages US: Closest friends: 10% of men name a woman, 32% of women name a man (Verbrugge, 1977) Gender & friendship networks
  36. 36. Real-life networks Homophily Only 8% of people have any people of another race that they discuss important matters with (Marsden, 1987) Race & friendship networks US: 1% of white marriages, 5% of black marriages, 14% of asian marriages (Fryer, 2006) Interracial marriages US: Closest friends: 10% of men name a woman, 32% of women name a man (Verbrugge, 1977) Gender & friendship networks In all cases lower than if ignoring attributes
  37. 37. Possible Explanations Reasons for Homophily Opportunity (Contact Theory): Benefits/Costs:
  38. 38. Possible Explanations Reasons for Homophily The possibility that you meet people could be biased by attributes (i.e, race) Opportunity (Contact Theory): Benefits/Costs: More of a chance of meeting your own type
  39. 39. Possible Explanations Reasons for Homophily The possibility that you meet people could be biased by attributes (i.e, race) Opportunity (Contact Theory): Benefits/Costs: More of a chance of meeting your own type Common attributes (i.e., language, culture, knowledge) make it easier
  40. 40. Possible Explanations Reasons for Homophily The possibility that you meet people could be biased by attributes (i.e, race) Opportunity (Contact Theory): Benefits/Costs: Also social pressure or social competition More of a chance of meeting your own type Important: Common attributes (i.e., language, culture, knowledge) make it easier
  41. 41. Possible Explanations Reasons for Homophily The possibility that you meet people could be biased by attributes (i.e, race) Opportunity (Contact Theory): Benefits/Costs: Also social pressure or social competition More of a chance of meeting your own type Common attributes (i.e., language, culture, knowledge) make it easier Important: The structure of the network depends on the characteristics i.e., why communication might circulate among one group and not another?
  42. 42. Quick Summary two points From Structuralism: From Homophily (Segregation Patterns):
  43. 43. Quick Summary two points From Structuralism: The characteristics of the network matter. They affect the individuals From Homophily (Segregation Patterns):
  44. 44. Quick Summary two points From Structuralism: The characteristics of the network matter. They affect the individuals From Homophily (Segregation Patterns): The characteristics of the individuals matter. They affect the structure of the network
  45. 45. Reciprocity
  46. 46. Reciprocity Local Patterns
  47. 47. Reciprocity Local Patterns Directed Networks
  48. 48. Reciprocity Local Patterns Directed Networks A node can be linked to another without the second being linked to the first (i.e., webpages)
  49. 49. Reciprocity Local Patterns Directed Networks A node can be linked to another without the second being linked to the first (i.e., webpages) ij in g does not imply ji in g
  50. 50. Reciprocity Local Patterns Directed Networks A node can be linked to another without the second being linked to the first (i.e., webpages) Reciprocity There is a tendency to dyadic reciprocation in most directed networks ij in g does not imply ji in g
  51. 51. Reciprocity Local Patterns Directed Networks A node can be linked to another without the second being linked to the first (i.e., webpages) Reciprocity There is a tendency to dyadic reciprocation in most directed networks ij in g does not imply ji in g if ij in g it is more likely ji in g
  52. 52. Reciprocity Explanation
  53. 53. Reciprocity Explanation Mutual Dependence
  54. 54. Reciprocity Explanation Mutual Dependence Actors (i.e., players, people) depend on each other for valued outcomes, and benefits will be received from another actor only if they are also given in return (Emerson, 1972)
  55. 55. Reciprocity Explanation Mutual Dependence Actors (i.e., players, people) depend on each other for valued outcomes, and benefits will be received from another actor only if they are also given in return Think about Cooperation: if relations are not reciprocated they are likely to be terminated more rapidly (Emerson, 1972)
  56. 56. Reciprocity Explanation Mutual Dependence Actors (i.e., players, people) depend on each other for valued outcomes, and benefits will be received from another actor only if they are also given in return Think about Cooperation: if relations are not reciprocated they are likely to be terminated more rapidly (Emerson, 1972) Keeping a non-reciprocated relation implies status deference tend to be eliminated
  57. 57. Node Centrality
  58. 58. Node Centrality Positions in Networks
  59. 59. Node Centrality Positions in Networks Who are influential, powerful (Think of our Facebook Example with Ana) How different nodes are positioned in the network?
  60. 60. Node Centrality Positions in Networks Who are influential, powerful (Think of our Facebook Example with Ana) How different nodes are positioned in the network? Many social networks show a fair extent of centralization
  61. 61. Node Centrality Positions in Networks Who are influential, powerful (Think of our Facebook Example with Ana) How different nodes are positioned in the network? Many social networks show a fair extent of centralization differentiation between social actors with respect to their centrality
  62. 62. Node Centrality Example Ways of measuring centrality
  63. 63. Node Centrality 2 1 4 1 2 4 1 3 3 2 3 2 2 2 Example Ways of measuring centrality
  64. 64. Node Centrality 2 1 4 1 2 4 1 3 3 2 3 2 2 2 Both white nodes have degree 2 (degree centrality) The first seems more central - neighbors (3) & (4): (betweenness) Better connected in another sense Example Ways of measuring centrality
  65. 65. Node Centrality 2 1 4 1 2 4 1 3 3 2 3 2 2 2 There are many other measures of centrality Both white nodes have degree 2 (degree centrality) The first seems more central - neighbors (3) & (4): (betweeness) Better connected in another sense Example Ways of measuring centrality
  66. 66. Node Centrality Why do we observe it? It reflects social organization and opportunities
  67. 67. Node Centrality Why do we observe it? It reflects social organization and opportunities A strongly centralized network increases the likelihood of collective action in mobilizations - easier contact to others (Marwell, Oliver & Prahl, 1988)
  68. 68. Node Centrality Why do we observe it? Result of feedback processes Favoring the creation of links to nodes that are already highly connected It reflects social organization and opportunities A strongly centralized network increases the likelihood of collective action in mobilizations - easier contact to others (Marwell, Oliver & Prahl, 1988)
  69. 69. Node Centrality Why do we observe it? Result of feedback processes Favoring the creation of links to nodes that are already highly connected It reflects social organization and opportunities A strongly centralized network increases the likelihood of collective action in mobilizations - easier contact to others (Marwell, Oliver & Prahl, 1988) Unto him that hath is given and from him that hath not is taken away, even that which he hath The Matthew effect (Merton, 1968)
  70. 70. Node Centrality Why do we observe it? Result of feedback processes Favoring the creation of links to nodes that are already highly connected It reflects social organization and opportunities A strongly centralized network increases the likelihood of collective action in mobilizations - easier contact to others (Marwell, Oliver & Prahl, 1988) Unto him that hath is given and from him that hath not is taken away, even that which he hath The Matthew effect (Merton, 1968) However, centralization is most likely in physical networks: Internet hubs
  71. 71. Checklist
  72. 72. Both structure of the network & individual behavior (and characteristics) influence each other Checklist
  73. 73. Both structure of the network & individual behavior (and characteristics) influence each other Checklist People use their social networks as a form of capital that helps them achieve what they want
  74. 74. Checklist People use their social networks as a form of capital that helps them achieve what they want Social networks portray different properties: Both structure of the network & individual behavior (and characteristics) influence each other
  75. 75. Checklist People use their social networks as a form of capital that helps them achieve what they want Social networks portray different properties: Individuals with common traits are likely to be related (Homophily) Most relationships are reciprocal (both parts aim for it) We can look locally at who is influential (centrality) Important for diffusion of information Both structure of the network & individual behavior (and characteristics) influence each other
  76. 76. Questions?

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