Dear all,
I have a long list of parties and participants over many years and want
to extract network relations between people to identify groups of
friends. My list looks like this:
Party 1; date party 1; first name 1 last name 1; first name 2 last name
2; first name 3 last name 3;
Party 2; date party 2; first name 1 last name 1; first name 3 last name
3; first name 4 last name 4;
Party 3; date party 3; first name 3 last name 3; first name 5 last name 5;
Party 4; date party 4; first name 2 last name 2; first name 6 last name
6; first name 3 last name 3; first name 1 last name 1;
Party 5; date party 5; first name 5 last name 5; first name 4 last name 4;
....
Obviously the amount and the order of names is not regular. The list is
far too long to count co-appearances for each person-person combination
by hand.
What I would like to do is first of all create a network with individual
persons as nodes and the co-appearances as edges and the number of
co-appearances as strenght of interactions clustering closesly related
people.
In a second step it would be beneficial to extract information on the
durability of these interactions by including the time difference
between first and last interaction.
Do you have any ideas or hints how to approach this problem?
Thank you so much,
Hendrik
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