[CollEc] CollEc App Offline

[Düben, Christian]

No, N is the number of authors in the respective graph. The main graph currently entails 45,594 people.

Let me illustrate the results with an example. Assume some author is located at an average distance of 4 from other authors in the graph. Then the total distance is: sum d = 45594 * 4 = 182376. Plugging this into the closeness formula, C = 1 / sum d, produces a closeness value of 1 / 182376 = 5.483178e-06. Transforming it to the former CollEc's definition means multiplying it by (sum d)^2 / N: (182376^2 / 45594) * 5.483178e-06 = 4.

Christian Düben
Research Associate
Chair of Macroeconomics
Hamburg University
Von-Melle-Park 5, Room 3102
20146 Hamburg
Germany
+49 40 42838 1898
christian.dueben at uni-hamburg.de
http://www.christian-dueben.com


-----Original Message-----
From: Christian Zimmermann <zimmermann at stlouisfed.org> 
Sent: Mittwoch, 30. Dezember 2020 16:22
To: Düben, Christian <Christian.Dueben at uni-hamburg.de>
Cc: Thomas Krichel <krichel at openlib.org>; CollEc Run <collec-run at lists.openlib.org>
Subject: Re: [CollEc] CollEc App Offline

And N would happen to be close to a million?

Christian Zimmermann                          FIGUGEGL!
Economic Research
Federal Reserve Bank of St. Louis
P.O. Box 442
St. Louis MO 63166-0442 USA
https://ideas.repec.org/zimm/   @CZimm_economist

On Wed, 30 Dec 2020, D�ben, Christian wrote:

> I do use binary paths here. Weighted paths are not exported.
>
> As far as I understand it, there is one fixed closeness formula: 1 / (sum d). Just like the definitions of the mean, the variance, and other statistical measures are fixed. Your alternative measure appears to be (sum d) / N. Thus, multiplying my results by d^2 / N should produce your results. This is not about different path defintions. The former CollEc's closeness values are simply a scaled version of the new CollEc's closeness values. How does this make my results counter-intuitive? There is nothing different about the underlying paths.
>
> Christian D�ben
> Research Associate
> Chair of Macroeconomics
> Hamburg University
> Von-Melle-Park 5, Room 3102
> 20146 Hamburg
> Germany
> +49 40 42838 1898
> christian.dueben at uni-hamburg.de
> http://www.christian-dueben.com
>
> -----Original Message-----
> From: Thomas Krichel <krichel at openlib.org>
> Sent: Mittwoch, 30. Dezember 2020 04:27
> To: D�ben, Christian <Christian.Dueben at uni-hamburg.de>
> Cc: CollEc Run <collec-run at lists.openlib.org>
> Subject: Re: [CollEc] CollEc App Offline
>
>  D�ben, Christian writes
>
>> Thomas, how do you get closeness values larger than 1?
>
>  I use common sense. The closeness of a person is the  average 
> distance from one to any other, for all others.
>  Since the distance between any pair is at least one,  the average 
> must be larger than one.
>
>> Do you scale the results by some factor?
>
>  No.
>
>> Or is the distance between co-authors not 1 in your case?
>
>  It is.
>
>> With the closeness equation of C(v) = 1 / (\sum_{i \neq v} d(v, i)) 
>> where d(v, i) is the length of the shortest cost path between author 
>> v and author i and d(v, i) \geq 1, any closeness value should be 
>> between
>> 0 and 1.
>
>  There is something counter-intuitive in this approach.
>
>  I said many times, if we don't use a binary model, we will leave our  
> users confused. Alternative weighing schemes should be used to  filter 
> out from binary short paths that have the same  length. However, the 
> way you do that will not have any impact on the  closeness, as 
> expressed in my common sense understanding. It will  only impact the 
> betweenness.
>
> --
>
>  Cheers,
>
>  Thomas Krichel                  http://openlib.org/home/krichel
>                                              skype:thomaskrichel
>
> _______________________________________________
> CollEc-run mailing list
> CollEc-run at lists.openlib.org
> http://lists.openlib.org/cgi-bin/mailman/listinfo/collec-run
>