3 ms·

But "it doesn't" can include things that are both distrubuted with fatter or thinner tails? And if it is normally distrubuted below some range, and then power

• marcosdumay 9y ago
  For the first question, yes. "Not power law" means anything else. But I am quite confused on what you mean with power laws "above" or "below" some range.
  • esrauch 9y ago
    It seems that the reason power laws are important is the frequency of arbitrarily large values, and the implications those have on data processing. Under a definition that strict it doesn't sound useful: imagine the most common degree / # of connections is 10 and 5 is less popular: isn't that already not a power law? Regardless of how the distribution behaves for degree 100, 1000, etc. It seems like under many models you have some unimportant distribution of low-degree connections and then close to a power law distribution if you consider above some threshold: in terms of algorithm selection and design under those conditions the dominating factor seems like it can typically still be just the frequency of large degrees which can still be close to a power law and rejected as one by this paper, right?
    • hammock 9y ago
      Yeah , I sort of agree that practically power laws are pretty useful, even if they are approximators. I think the paper is trying to say though, the same people (including myself) who use power law to generate these models often say that these processes are actually power law processes. When in fact they may not be. Which is surprising to me (Zipfs law and all of that)