Difference between revisions of "MCSN Tuesday, 1-Nov-11"

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= Quiz =
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= Quiz #2 take 2=
* Success! Nearly everyone did much better than before.
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* A pedagogical success. Nearly everyone did ''much'' better than before.
* page 1 - bravo!
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* page 1 - bravo! (perhaps one or two arithmetic mistakes, or creating a semiwalk that was also a semipath...)
* page 2, mainly bravo, but a few common misconceptions remain:
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* page 2, also bravo, mostly - but a few common misconceptions remain:
 
** components can't overlap (because they're maximal)
 
** components can't overlap (because they're maximal)
 
** cores:  can't be determined from degree.  For one thing, a vertex in the 4-core has to be connected to at least 4 others in the 4-core (by definition!).  Therefore the smallest 4-core will have 5 vertices.  Some people indicated a ''single node'' as belonging to the 4-core.
 
** cores:  can't be determined from degree.  For one thing, a vertex in the 4-core has to be connected to at least 4 others in the 4-core (by definition!).  Therefore the smallest 4-core will have 5 vertices.  Some people indicated a ''single node'' as belonging to the 4-core.

Revision as of 08:29, 1 November 2011

Quiz #2 take 2

  • A pedagogical success. Nearly everyone did much better than before.
  • page 1 - bravo! (perhaps one or two arithmetic mistakes, or creating a semiwalk that was also a semipath...)
  • page 2, also bravo, mostly - but a few common misconceptions remain:
    • components can't overlap (because they're maximal)
    • cores: can't be determined from degree. For one thing, a vertex in the 4-core has to be connected to at least 4 others in the 4-core (by definition!). Therefore the smallest 4-core will have 5 vertices. Some people indicated a single node as belonging to the 4-core.
    • cliques: are defined to be maximal. So a triad isn't necessarily a clique, though if it's not a clique on its own it must be part of a larger clique. Note also that a square is not a clique unless it contains its diagonals.

4.8

  • How to define the flying teams?

Affiliation networks

Ideas

  • People affiliate to groups (often defined by space, like the University of Alberta), and events (typically defined by space-time, like this class session), whether by choice or circumstance.
  • Such affiliations define bipartite networks comprising two kinds of vertex, which we can call actors and events (don't be confused - events could be more like groups)
  • In a bipartite network there are two kinds of vertex, type A and type B. All lines connect a type A vertex to a type B vertex - there are no direct connections between vertices of type A, nor are there direct connections between vertices of type B.
  • A bipartite network is also called "two mode", since there are two kinds of vertex, and is represented by a matrix rectangle rather than a square (see this in Excel)
  • Affiliations define social circles which overlap.
  • Network representation of identity as a model for social belonging:
    • Culture model (common in traditional ethnomusicology): each individual belongs to one "complex whole" as Tylor put it in 1847.
    • Identity model (more common in sociology and contemporary ethnomusicology): each individual associates with multiple "simple parts", each person in a slightly different way. These "parts" can be viewed as social circles whose intersection is the individual.
    • Note: social identity can't be captured in a single Pajek partition....why? The concept of partition is closer to the traditional "culture" model of exclusive all-encompassing identities.
  • Social circles may also imply power circles with critical implications for relationships among "events" (groups). Example: Interlocking directorates
  • Typical assumptions about affiliation networks (critique! test!) (see p. 101):
  1. Affiliations are institutional or structural - less personal than friendships or sentiments. [What do you think? How could we test this?]
  2. "Although membership lists do not tell us exactly which people interact, communicate, and like each other, we may assume that there is a fair chance that they will." [what factors might impact the chances of actual dyadic interaction?]
  3. Actors at the intersection of multiple social circles...
    1. tend to interact even more
    2. enable indirect communication/control between the circles as a whole.
  4. "Joint membership in a social circle often entails similarities in other social domains." (i.e. homophily principle...Cause or effect?)
  • Representing two-mode networks with rectangular matrices
    • Rows represent first mode (e.g. actors)
    • Columns represent second mode (e.g. events)
  • Deriving one-mode network from two-mode network.
    • Mapping the "hidden networks" implied by two-mode network (under assumptions above) can be highly significant
    • One-mode network derived from rows (e.g. actors)
    • One-mode network derived from columns (e.g. events)

Applications: creating and manipulating two mode networks

Line values