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You have your nodes in two groups. It seems you want to partition your
collection of links into two networks - those linking to pairs in the
same classes and those to pairs in different classes. I think of this
as two separate calculations or transformations. Assume that the
original network is called is_linked_to, then we want to calculate two
new networks: is_similar_to and is_dissimilar_to. All three networks
may have the same nodes and is_similar_to links a->a and b->b whereas
is_dissimilar_to links a->b and b->a.
This can be done in VisuaLyzer of the SocioMetrica Suite at
http://mdlogix.com/visualyzer.htm in a somewhat straightforward manner
if your data can be exported from Pajek to UCINET format and then
imported into VisuaLyzer. If your nodes have two attributes, a name
and a class, then your partition is based on this second attribute. In
the reasoning language this second attribute is selected with the
relation, node^2-2. This effectively says 'Of the two attributes of the
node, give me the second.' (Similarly, node^2-1 returns the name.)
Alternatively, converse(node^2-2), constructs the second attribute. The
composition of the two (the relative product) is
(node^2-2:converse(node^2-2)); it is smaller than the identity relation.
Therefore we can intersect this with the original to define
is_similar_to as the product
is_linked_to*(node^2-2:converse(node^2-2)). Procedurally, this is
computed in VisuaLyzer by first typing this into the box called
'Relation Expression' brought to the top with the ^R command. Be sure
to rename the resulting expression as is_similar_to and check the box
labelled 'Extend the existing collection with this new network'.
Also, we may define is_dissimilar_to as
is_linked_to*(node^2-2:di:converse(node^2-2)) since the apartness
relation is di. All this results in three networks linking the same
set of nodes. Some understanding may come by observing that
is_linked_to is the sum (union) of the other two collections of links.
I hope this helps. This may require some experimentation, a
fundamentally different way of thinking, and more discussion.
jenine harris wrote:
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> Hi networkers,
> I have a large-ish directed network (1877 nodes) in Pajek with a
> that classifies each node into one of two categories. For the most
> part the
> nodes in each category only are connected to other nodes in the same
> category (Category A --> Category A). However, there is a proportion of
> nodes that are linked to nodes in the other category (Category A -->
> Category B). Is there a way in Pajek to extract/identify these nodes that
> are involved in cross-category connections? I've gone through the book
> have moderate experience with the software and still just can't come
> up with
> anything. My final goal here is to make a new partition with Category A,
> Category B, and Category AB which would be those nodes involved in cross
> category connections.
> I also have access to UCINET, but am much more comfortable with Pajek.
> Thanks in advance for your help.
> See you in Corfu!
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