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Binomial distribution for distribution_compare #48
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Thanks for using Your intuitions are good. However, try plotting the two degree distributions so you see the data you're asking the statistical test to deal with. Particularly try plotting both the PDF and the CCDF, then overlay |
Also when looking at graphs i plotted myself, both pdf and ccdf of scale-free and random networks seem to be easily discernible: |
Great! Now plot the fitted power law for |
I don't think this is necessary. I can see that a statistical test could fit a power law to this graph. My question is however why we don't use a binomial distribution for comparison, which would fit the graph unarguably better than a power_law. It would be close to a perfect fit on all data points. Look eg at ccdf_loglog.pdf. For a power law to fir that curve it would have to choose a pretty high min_x and would still not hit the data points in a better way than the actual original distribution (binomial) would. Is it hard to implement? Is the binomial distribution not well defined for the ccdf? Does the exponential / stretched exponential distribution already cover it? Or was it simply not deemed useful to implement it? |
Visualizing the fitted power law for So, essentially, you're taking the tail of an exponential distribution, chopping off the tail that's near vertical, fitting a power law to that, and then asking if that near-vertical tail is better described by a power law or an exponential (or the other functional forms you tested). This is not what you want. The ability to notice undesirable values for tl;dr: Set As for no binomial distribution being implemented: It would be very welcome! |
Thanks for the elaborate response. unfortunately setting But apparently the binomial distribution is not needed, as the exponential does fit very well for most small xmin. I guess the issue can then be closed. For the example I gave, I observe the following behavior when comparing 'powerlaw' vs 'exponential' distribution for different xmin: xmin 1 to 7: power-law misclassified as exponential (p-val < 0.0001) So in the end it comes down to picking a good Thanks for all the help! |
Glad to help! A few things to consider:
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My goal is to find the point where scale-free networks become indistinguishable from random (non-scale-free) networks.
I would expect something like the binomial distribution to be implemented for comparison using distribution_compare().
Is there a specific reason it wasn't implemented?
For example I tried the following code to distinguish between an obviously scale-free network and an obviously non-scale-free network (both with similar numbers of nodes/edges):
The output suggested that none of the implemented distributions provided a tool to discern between an preferential attachment model and a gnp random graph:
I am asking as i am no statistics expert and I might not see the significance of all the available distributions. But they seem to fail this basic example. I would be happy to help implement a distribution, that successfully fits a random gnp network. Or are there some limitations which make this hard/impossible?
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