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I have a data structure:
A>B and Length
Where A is anything uniquely identifiable as well as B.
Lets for the sake of simplicity A is a person and ...
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 08192013 #1
 Join Date
 Aug 2013
 Posts
 11
Hypothetical Database Program.
I have a data structure:
A>B and Length
Where A is anything uniquely identifiable as well as B.
Lets for the sake of simplicity A is a person and B is a person.
The arrow represents the act of giving an eye wink to acknowledge by A to B.
Length is the duration of wink.
Iam using the assumption that everyone is 6 degrees of separation from each other.
If I used this associative array or hash table algorithm, would I be able to map out who knows who and how close one person is to another person from a degree standpoint as well as influence?
Loop {
readline(ABfile)
winkcount{A~B} = winkcount{A~B} + 1
winksum{A~B} = winksum{A~B} +Length
}
foreach key in winksum {
if key in degrees
split key into A and B
foreach key in degrees
if key ends in A and degrees{key} < 10 # could be 7 but just in case
newkey = join key, ~, B
degrees{newkey} = degrees{key} + 1
endif
else
degrees{key} = 1 degree of separation
endif
}
query degrees where key starts with A and ends with B
prioritize query by ascending order of degrees and descending order of winksum and winkcount.
Will this produce what I'm trying to prove?
What happens when you find the key with fewest degrees of separation and highest order of winkcount and winksum?