A new RePEc service: CollEc

A new RePEc service is now on-line, CollEc. The main goal of this initiative is to analyze co-authorship networks within Economics. To this end, it collects all the authorship data from the RePEc Author Service and computes the shortest path through co-authorship relationships between any two registered economists. From all this data, two “features” are computed.

First, a closeness and a betweenness score is computed for every economist. Closeness measure how close one is with everyone else. Betweenness measures how frequently shortest paths have a particular economist as a node. Of course, economists can be ranked according to both criteria.

Second, the website allows to display the shortest paths between any two economists, and one can be surprised at how short they often are. To play with this, either navigate the lists on CollEc or find the direct link to an author’s page on IDEAS (author profile, under “statistics”), then enter the name of another author.

Note that only authors registered with RePEc are considered. Also, not every registered author is part of this global network of co-authorship. For example, an author without a (registered) co-author is excluded. Also, an economist at the end of a path cannot have a betweenness score, mostly likely someone with a single (registered) co-author.

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2 Responses to A new RePEc service: CollEc

  1. jornaltmann says:

    Dear Christian,

    it is doubtful that the closeness and betweenness is a good measure for evaluating the performance of researchers. Our research results showed that closeness and betweenness is not significantly correlated with the g-index of researchers (though other social network measures are). Even more, the results show that well-performing researchers do not have many joined publications. Please have a look at the abstract of our paper for details http://ideas.repec.org/p/snv/dp2009/201176.html.

    Best wishes,
    Jorn

  2. Interesting paper. I have not yet done the correlations analysis, so I cannot judge on our data. It will be part of the upcoming revision of the documentation for the rankings. But I would not want a strongly correlated indicator anyway, as it would not bring new information. Also, closeness and betweenness are intuitive and easy to understand (and they provide clear incentives for authors to get their co-authors to sign up with the RePEc Author Service.

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