Social Sub-groups
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Social Sub-groups. Overview Substantive papers: Wayne Baker Social structure in a place where there should be none Scott Feld What causes clustering in a network? Opportunity and interests Methods: Continue discussion of social subgroups: - Cluster analysis Roles & Blockmodels.

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Social Sub-groups

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Social Sub-groups

  • Overview

  • Substantive papers:

  • Wayne Baker

    • Social structure in a place where there should be none

  • Scott Feld

    • What causes clustering in a network? Opportunity and interests

  • Methods:

    • Continue discussion of social subgroups:

    • - Cluster analysis

  • Roles & Blockmodels

  • Social Sub-groups

    Wayne Baker: The Social Structure of a National Securities Market:

    1) Behavioral assumptions of economic actors

    2) Micro-structure of networks

    3) Macro-structure of networks

    4) Price Consequences

    Under standard economic assumptions, people should act rationally and act only on price. This would result in expansive and homogeneous (I.e. random) networks. It is, in fact, this structure that allows microeconomic theory to predict that prices will settle to an optimal equilibrium

    Baker’s Model:

    Baker’s Model:

    He makes two assumptions in contrast to standard economic assumptions:

    a) that people do not have access to perfect information and

    b) that some people act opportunistically

    He then shows how these assumptions change the underlying mechanisms in the market, focusing on price volatility as a marker for uncertainty.

    The key on the exchange floor is “market makers” people who will keep the process active, keep trading alive, and thus not ‘hoard’ (and lower profits system wide)

    Baker’s Model:

    Micronetworks: Actors should trade extensively and widely. Why might they not?

    A) Physical factors (noise and distance)

    B) Avoid risk and build trust

    • Macro-Networks: Should be undifferentiated. Why not?

      • A) Large crowds should be more differentiated than small crowds. Why?

    • Price consequences: Markets should clear. They often don’t. Why?

      • Network differentiation reduces economic efficiency, leading to less information and more volatile prices

    Baker: Use frequency of exchange to identify the network, resulting in:

    Baker finds that the structure of this network significantly (and differentially) affects the price volatility of the network

    Baker: Because size is the primary determinant of clustering in this setting, he concludes that the standard economic assumption of large market = efficient is unwarranted.

    Scott Feld: Focal Organization of Social Ties

    Feld wants to look at the effects of constraint & opportunity for mixing, to situate relational activity within a wider context.

    The contexts form “Foci”,

    “A social, psychological, legal or physical entity around which joint activities are organized” (p.1016)

    People with similar foci will be clustered together. He contrasts this with social balance theory.

    Claim: that much of the clustering attributed to interpersonal balance processes are really due to focal clustering.

    (note that this is not theoretically fair critique -- given that balance theory can easily accommodate non-personal balance factors (like smoking or group membership) but is a good empirical critique -- most researchers haven’t properly accounted for foci.)

    Cluster analysis

    • As with the network cluster algorithms, there are many options for clustering. The three that I use most are:

      • Ward’s Minimum Variance -- the one I use almost 95% of the time

      • Average Distance -- the one used in the example above

      • Median Distance -- very similar

    • Again, the SAS manual is the best single place I’ve found for information on each of these techniques.

    • Some things to keep in mind:

    • Units matter. The example above draws together pairs horizontally because the range there is smaller. Get around this by standardizing your data.

    • This is an inductive technique. You can find clusters in a purely random distribution of points. Consider the following example.

    Cluster analysis

    The data in this scatter plot are produced using this code:

    data random;

    do i=1 to 20;






    Cluster analysis

    Resulting dendrogram

    Cluster analysis

    Resulting cluster solution

    Cluster analysis

    Cluster analysis works by building a distance matrix between each pair of points. In the example above, it used the Euclidean distance which in two dimensions is simply the physical distance between the points in a plot.

    Can work on any number of dimensions.

    To use cluster analysis in a network, we base the distance on the path-distance between pairs of people in the network.

    Consider again the blue-eye hip example:

    Cluster analysis

    Distance Matrix

    0 1 3 2 3 3 4 3 3 2 3 2 2 1 1

    1 0 2 2 2 3 3 3 2 1 2 2 1 2 1

    3 2 0 3 2 4 3 3 2 1 1 1 2 2 3

    2 2 3 0 1 1 2 1 1 2 3 3 3 2 1

    3 2 2 1 0 2 1 1 1 1 2 2 3 3 2

    3 3 4 1 2 0 1 1 2 3 4 4 4 3 2

    4 3 3 2 1 1 0 2 2 2 3 3 4 4 3

    3 3 3 1 1 1 2 0 1 2 3 3 4 3 2

    3 2 2 1 1 2 2 1 0 1 2 2 3 3 2

    2 1 1 2 1 3 2 2 1 0 1 1 2 2 2

    3 2 1 3 2 4 3 3 2 1 0 1 2 2 3

    2 2 1 3 2 4 3 3 2 1 1 0 1 1 2

    2 1 2 3 3 4 4 4 3 2 2 1 0 2 2

    1 2 2 2 3 3 4 3 3 2 2 1 2 0 1

    1 1 3 1 2 2 3 2 2 2 3 2 2 1 0

    Cluster analysis

    The distance matrix implies a space that nodes are embedded within. Using something like MDS, we can represent the space implied by the distance matrix in two dimensions. This is the image of the network you would get if you did that.

    Cluster analysis

    When you use variables, the cluster analysis program generates a distance matrix. We can, instead use the network distance matrix directly. If we do that with this example network, we get the following:

    Cluster analysis

    Cluster analysis

    In SAS you use two commands to get a cluster analysis. The first does the hierarchical clustering. The second analyzes the cluster output to create the tree.

    Example 1. Using variables to define the space (like income and musical taste):

    procclusterdata=a method=ave out=clustd std;

    var x y;

    id node;


    proctreedata=clustd ncl=5out=cluvars;


    Cluster analysis



    /* blue eye example */



    /* lines cut here */



    mattrib dmat format=1.0;

    print dmat;




    create ddat from ddat; /* creates the dataset */

    append from ddat;


    data ddat (type=dist); /* tells SAS it is a distance */

    set ddat; /*matrix*/


    Example 2. Using a pre-defined distance matrix to define the space (as in a social network).

    You first create the distance matrix (in IML), then use it in the cluster program.

    Cluster analysis

    Example 2. Using a pre-defined distance matrix to define the space (as in a social network).

    Once you have it, the cluster program is just the same.

    procclusterdata=ddat method=ward out=clustd;

    id col1;


    proctreedata=clustd ncl=3out=netclust;

    copy col1;



    tables cluster;



    var col1 cluster;


    The CROWDS algorithm combines the density approach above with an initial cluster analysis and a routine for determining how many clusters are in the network. It does so by using the Segregation index and all of the information from the cluster hierarchy, combining two groups only if it improves the segregation fit for both groups.

    The one other program you should know about is NEGOPY. Negopy is a program that combines elements of the density based approach and the graph theoretic approach to find groups and positions. Like CROWDS, NEGOPY assigns people both to groups and to ‘outsider’ or ‘between’ group positions. It also tells you how many groups are in the network.

    It’s a DOS based program, and a little clunky to use, but NEGWRITE.MOD will translate your data into NEGOPY format if you want to use it.

    There are many other approaches. If you’re interested in some specifically designed for very large networks (10,000+ nodes), I’ve developed something I call Recursive Neighborhood Means that seems to work fairly well.

    The Crowds Algorithm

    1. Identify members of network bicomponents, remove people not included.

    2. Cluster the reduced network.

    - Identify optimal number of groups: (TREEWALK)

    - For each level of the cluster partition tree do (BFS):

    -Move up the tree from smaller to larger groups.

    -If the fit for both groups is improved by joining them then do so.

    -If not, then identify group at that level.

    -End TREEWALK.

    Do until all groups are identified (GLOBAL LOOP):

    3. Evaluate node fit.

    Do until nodes cannot be moved:

    For each identified cluster do (GRPCHECK):

    - Ensure group is a bi-component.

    -Calculate effect on group a of moving node j to group a.

    -Calculate effect on j's present group of removing j.

    - If there is a positive net gain to moving j from own group to a, then do so.


    4. Identify Bridging members.

    -If removing j from group a would improve the fit of group a, AND assigning j to any other group would lower the fit for that group, then j is considered a bridge. Place all bridges in separate class.

    5. Group Check.

    Check returns to combining groups. IF merging groups would improve the fit of all groups to be merged, then do so.

    - Evaluate bridges, to be sure that they are not bridging two groups that have now merged.

    End Global loop.

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