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On Tue, Jul 12, 2005 at 05:45:40PM -0700, Corey Phelps wrote:
> 1) He has data on the relationships (presence/absence) among nearly 1
> million individuals. He would like to store this in a single, flat file
> as an adjacency matrix.

Don't.  Since the networks is certainly sparse (few individuals are
connected to a million others), use adjacency lists:

node1 neighbor1_of_1 neighbor2_of_1 neighbor3_of_1 ...
node2 neighbor1_of_2 neighbor2_of_2 neighbor3_of_2 ...
...

> Furthermore, he needs to be able to calculate
> network measures on the individuals in this network (e.g., between
> centrality). I have never worked with a data set this large. Is this
> possible? If so, what file format would work and what SNA program or
> programming language should he use?

It all depends on the actual number of links present.  If they are also
in the order of a few million, you are fine.  As usual, Pajek is a good
suggestion.

> 2) In addition to the relational data, he also has data on an attribute
> of each individual (coded as dichotomous: present/absent). He would like
> to be able to combine these two types of data in order to calculate the
> path length between a focal individual and an individual who possesses
> the attribute. For example, if person A is connected to person B (who
> has the attribute), the path length would be 1. If person A is connected
> to person B (who does NOT have the attribute), who is connected to
> person C (who has the attribute), thent he path length would be 2. He
> would like to use the ability to calculate such path lengths to
> calculate a type of Information Centrality (Stephenson & Zelen, 1989)
> for each actor in the network. This measure would only consider the path
> lengths between ego and those alters who possess the attribute. If you
> have recommendations on how to do any of these steps and/or recommended
> references, please let me know.

What you describe is closeness for a subset of targets (those possessing
the attribute), so simple breadth-first search will do (see any introduction
to algorithms).

Regards,

B.

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