Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Changing s-parameter in PAFit #4

Open
eglantine-coder opened this issue Aug 19, 2021 · 5 comments
Open

Changing s-parameter in PAFit #4

eglantine-coder opened this issue Aug 19, 2021 · 5 comments

Comments

@eglantine-coder
Copy link

Hi,

I've been getting very high s-values when running the PAFit model on my data. How can I decide the value, especially when wanting to compare results for different datasets?

I've tried changing the parameters within the PAfit function but it doesn't work.

Thanks

@thongphamthe
Copy link
Owner

Hi,
A high value of s is perfectly fine. It means that node fitnesses are highly concentrated.
For interpretations, instead of s, it may be better to look at the estimated attachment exponent and the estimated fitness distribution.

@eglantine-coder
Copy link
Author

Hi,

Thank you for the quick reply.

I have attached two examples of the results I got for the PAFit model on two different datasets. How could I interpret the 1.06 value for United States vs. the 53.8 for UAE in terms of PA and fitness ? Are they comparable at all ?

Many thanks

results

results

@thongphamthe
Copy link
Owner

Hi,
Fitnesses (and PA) from different datasets are not comparable.

@eglantine-coder
Copy link
Author

eglantine-coder commented Aug 23, 2021

Hi,

Then, how could we compare the estimated attachment exponent and the estimated fitness distribution, given the following results, for instance?
stats
stats

Thanks

@thongphamthe
Copy link
Owner

thongphamthe commented Aug 25, 2021

The strengths of the PA effect and the fit-get-richer effect can be compared between different datasets.

In your example, one can compare the estimated attachment exponents and say that the PA effect in the second dataset is stronger than the PA effect in the first dataset.

For comparing the fit-get-richer effect, one way is to compare the variances of the estimated fitness distributions. A larger variance implies a stronger fit-get-richer effect.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

2 participants