Some of us have been noodling around...
Open User Community Development
http://www.w3.org/2008/OUCD/wiki/Main_Page
I found a python implementation of the advogato trust metric,
but I'm struggling to get my head around it.
So I wrote a little piece of code to simulate growth of
a social network; people join, and they friend/follow/certify
others with certain probabilities. Also, with some
probability, they joined the network to exploit it
rather than to contribute; i.e. they're evil.
Evil folks sometimes certify other evil folks,
but contributors know better. The python code writes
out each step of the simulation in JSON.
Then some javascript code, using Raphael, animates it.
Contributors are blue; spammers are red.
The outcome of the trust metric calculation is a 1 or 0
after the name; it represents whether the agent is
certified or whatever.
To get the code and run it (assuming you have
both hg and bzr installed):
connolly@pav:~/projects$ hg clone http://bitbucket.org/DanC/socialsim/
destination directory: socialsim
requesting all changes
adding changesets
adding manifests
adding file changes
added 12 changesets with 22 changes to 13 files
updating working directory
13 files updated, 0 files merged, 0 files removed, 0 files unresolved
connolly@pav:~/projects$ cd socialsim/
connolly@pav:~/projects/socialsim$ bzr branch lp:dracula
Branched 12
revision(s).
connolly@pav:~/projects/socialsim$ mv dracula/js dracula-js
connolly@pav:~/projects/socialsim$ python socialsim.py >,states.js
connolly@pav:~/projects/socialsim$ firefox socialpg.html
Then click "Next" to see the steps.
I'm attaching screenshots of a few steps. Note how the evil
red circles remain around the edges of the network and
never manage to penetrate toward the middle.
--
Dan Connolly, W3C http://www.w3.org/People/Connolly/
gpg D3C2 887B 0F92 6005 C541 0875 0F91 96DE 6E52 C29E
