Hi Sebastian,
Thanks for your input.
Appreciate it!
Cheers
Pascal
Am 31.10.13 21:21 schrieb "Sebastian Schelter" unter
:
>Hi Pascal,
>
>This paper has a very nice overview of several link predictions
>algorithms:
>
>http://www.cs.cornell.edu/home/kleinber/link-pred.pdf
>
>Best,
>Sebastian
>
You could also approach the problem from a statistical point of view and
sample from an inferred distribution of the links (which vertices they
link). The prior distribution probably won't be as interesting as the
conditional distributions you are most likely interested in...that is,
start with so
Hi Pascal,
This paper has a very nice overview of several link predictions algorithms:
http://www.cs.cornell.edu/home/kleinber/link-pred.pdf
Best,
Sebastian
On 31.10.2013 13:55, Claudio Martella wrote:
> I would assume that it depends on your data. A graph is a very general
> structure, and it
Hi,
You can look at the Facebook Link Prediction Challenge on Kaggle where you
have to suggest links in a social Network.
The link for the forum for the contest is:
http://www.kaggle.com/c/FacebookRecruiting/forums
It has a lot of interesting approaches. One of them can be found at the
link below:
I would assume that it depends on your data. A graph is a very general
structure, and it is difficult to attack this problem in general. The most
obvious one is transitive closure (if A is connected to B and B to C then A
could be conntected to C). The triangle counting example in our codebase
(alt
